Body composition and metabolic profile during chemotherapy in early-stage breast and cervical cancer patients in Douala, Cameroon: A hospital-based study

Body composition and metabolic profile during chemotherapy in early-stage breast and cervical cancer patients in Douala, Cameroon: A hospital-based study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Body composition and metabolic profile during chemotherapy in early-stage breast and cervical cancer patients in Douala, Cameroon: A hospital-based study Dominique Anaba, Wilfried Steve Ndeme Mboussi, Ester Dina Bell, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4564004/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Changes in body composition during chemotherapy can negatively influence the prognosis of cancer patients. In order to assess changes in body composition in patients undergoing chemotherapy, a case-control study was conducted in the cobalt therapy departments of the Douala General Hospital. The overall objective of this study was to determine the impact of chemotherapy and stage of disease on changes in body composition in women with breast or cervical cancer followed at the oncology unit of Douala General Hospital. Muscle mass, body fat and body water percentages were measured by the bioimpedancemetry method and blood samples were collected for the measurement of albumin and creatinine concentrations. The results were analysed using SPSS version 16 for Windows (SPSS, IBM, Chicago, IL, USA). The mean age of the patients was 44.62 ± 11.23 years for breast cancer (BC) patients, 50.37 ± 10.78 years for cervical cancer (CC) patients and 46.11 ± 10.43 years for controls. Muscle mass, body fat and body water decreased significantly in cases compared to controls (respectively p = 0.0028, p = 0.004, p = 0.004). According to the stage of the disease when the two clinical groups were taken individually muscle mass decrease significantly between stage 1 to stage 2 in patients with BC (p = 0.001), but not in patient with CC (p = 0.84). Body fat and body water percentages decrease not significantly between stage 1 to stage 2 in the both cancer. Metabolically, creatinine concentrations were significantly elevated in both groups of patients compared with controls (p < 0.001), and albumin concentrations were significantly low (p < 0.001). In terms of disease stage, creatinine concentrations increased but not significantly between stage 1 and stage 2 in breast cancer patients (p = 0.08) and decreased non-significantly in cervical cancer patients (p = 0. 95). Albumin concentrations decreased significantly in cervical cancer patients (p = 0.01) between stage 1 and stage 2 but did not decrease significantly in breast cancer patients (p = 0.55). In conclusion, chemotherapy considerably altered the physical and metabolic body composition of breast and cervical cancer patients included in our study. Chemotherapy Body composition bioimpedancemetry cancer Figures Figure 1 Figure 2 Figure 3 INTRODUCTION changes in body composition during chemotherapy have an impact on patients' vital prognosis, as several studies have shown ( 1 – 7 ). The biochemical and anatomical model of body composition as described by Kaffel et al in 2021 demonstrates a model for analysing body composition, the components of which, notably adipose, muscle and water, are of vital importance during chemotherapy( 8 ). Body water volume during chemotherapy is of vital importance in the elimination of therapeutic molecules and the maintenance of optimal renal clearance( 9 – 11 ), such as muscle mass, which has been shown to help regulate the immune system, reduce the side-effects of chemotherapy, and have a positive influence on vital prognosis ( 12 , 13 ). Adipose tissue has been described as a poor marker of vital prognosis during chemotherapy, given its involvement in the production of reactive oxygen species and drug toxicity( 14 , 15 ). These changes in body composition during chemotherapy are not only physical but also metabolic. Certain biomarkers of muscle metabolism such as creatinine, vitamin D, leptin and albumin could be involved. Study of creatinine clearance has demonstrated its value in cancer chemotherapy. It has been described as a predictive biomarker of the side effects of chemotherapy ( 16 ), of the toxicity of certain therapeutic molecules ( 17 ), and as a marker of overall survival ( 18 ). Albumin is also used as a prognostic and survival factor during chemotherapy( 19 , 20 ). Chemotherapy, whether adjuvant or neoadjuvant, is the most widely used cancer treatment in Cameroon, particularly for breast and cervical cancer. These cancers are the most deadly among women worldwide, but also in Cameroon, where they caused 2,108 and 1,787 deaths respectively in 2020 ( 21 ). The treatment of these cancers in Cameroon is characterised by a very low therapeutic index and the majority of patients who start therapy die 12 months after the start of therapy. Knowing the impact of changes in body composition on the response to chemotherapy, it is important for us to investigate its profile in patients undergoing chemotherapy. The general objective of this study was to determine the impact of chemotherapy and stage of disease on changes in body composition in women with breast or cervical cancer followed at the oncology unit of Douala General Hospital. MATERIAL AND METHODS Study site The study was conducted from November 2023 to April 2024 at the oncology department of Douala General Hospital (4° 03’ 55’’NORD, 9° 45’ 31’’EST), Littoral region, Wouri division, Cameroon. Douala General Hospital is one of five first-class hospitals in Cameroon. The hospital has a myriad of general and specialist medical services, including an oncology department. The latter comprises three units specialized in the diagnosis and treatment of cancer, including surgery, radiotherapy, and chemotherapy. As the treating oncologist at this hospital, our research team chose it as the place to study because of the large number of patients we receive from all over Cameroon and Central Africa. Study population The study included three groups of women. The first two groups consisted of women with breast or cervical cancer whose histological and biological diagnosis had been confirmed by me or by one of the colleagues in the department undergoing chemotherapy, or who had been transferred by other oncologists in the country or in the Central African sub-region for follow-up in our hospital. The third group of women was made up of women with no type of cancer or with no clinical or symptomatological signs of a disease recurrent within the hospital, who could be nurses, care assistants or sick call nurses who had given their informed consent to take part in the study. the other women, whether ill or not, who refused to take part in the study by giving their informed consent were not included in the study, and this in no way changed the quality of the service provided. The sample size was defined by convincing. We included one hundred and nine women in our study. Study design To achieve our objective, we conducted a case-control study to better assess the impact of chemotherapy on body composition. Cases recruited as women newly diagnosed with breast cancer or cervical cancer having freely and informedly agreed to participate in our study; controls defined as women without cancer and meeting the criteria of good health defined according to the WHO ( 22 ), recruited within the said hospitals (nurses, care assistants, nursing staff). Data collection A structured questionnaire was used to collect sociodemographic, clinical, body composition, creatinine and albumin concentrations data from the patients after signing the informed consent. The questionnaire was completed after explaining the purpose of the study during a 10–15 minutes interview with the patient. The first part was designed to collect the patients' sociodemographics informations and body composition parameters, including marital status, level of study, sector of activity, profession, town of residence, age, muscle mass, body fat and body water percentage. The second part collected clinical and therapeutics informations on the disease (type of cancer, stage, presence or absence of metastases, treatment protocol, and dose of the drug used). The final part collected the information about creatinine and albumin concentration. - Measuring the components of body composition Muscle mass, body fat an body water percentages have been measured by bioimpedance analysis (BIA) using a calibrated system of equations by DXA (Dual-energy X-ray absorptiometry) to calculate muscle mass as proposed by Janssen et al ( 23 ), body fat percentage ( 24 ) and body water percentage ( 25 ). - Measuring of creatinine and albumin concentrations Four milliliters (04 ml) of blood was collected from each patient by venipuncture using Eclipse safety collection needles with port-tube in sterile dry tubes that were labeled and stored in an appropriate cooler. The blood samples were then transported the same day to laboratory for biological analysis, then centrifuged at 3000 rpm. The sera obtained were transferred into Eppendorf’s tubes for creatinine and albumin determination. Creatinine measurement was performed according to the method described by Jaffé ( 26 ). And albumin was measured using the colorimetric method ( 27 ). Ethical considerations This study was conducted according to the guidelines for clinical research on experimental models for clinical research on humans, as indicated by the Ministry of Public Health of Cameroon. Administrative authorizations were issued by the institutional human health research ethics committee of the University of Douala (N° 3050 CEI-Udo/04/2022/T) and the Douala General Hospital (N°458 AR/MINSANTE/HGD/DM/08/22). The aim and objectives of the study were explained to each participant in the language they understood best (French or English). Only participants who signed an informed consent form were admitted to the study. Participation in the study was voluntary, and women were free to refuse to answer all relevant questions and to withdraw from the study if they wished at any time. Also, there was no difference in management between women who agreed to take part in the study and those who did not. Each participant was made aware of the importance of practising physical activity during therapy. Statistical analyses Data were entered into an Excel sheet (Microsoft Office, USA) and subsequently analysed with SPSS version 16 for Windows (SPSS, IBM, Chicago, IL, USA). The qualitative and quantitative variables were presented as mean ± standard deviation (SD) and percentage, respectively. The one-way analysis of variance (ANOVA) was used to compare means and subsequently Duncan's post hoc test was used to make pairwise comparisons. The non-parametric Mann-Whitney test was used to make comparisons when the ANOVA could not be used. The Pearson correlation was used to study the relationship between the different parameters. The significance level was set at P < 0.05. RESULTS Selection procedure for newly diagnosed cancer patients included in the study 120 women were recruited during the study period. 60 were women newly diagnosed with breast cancer ( 30 ) or cervical cancer ( 30 ) and 60 were control women recruited within the hospital. During data collection, one (01) breast cancer patient and three (03) cervical cancer patients could not be sampled due to anaemia. Among the female controls, seven (07) did not meet our matching criteria including age and body mass. Four (04) and seven (07) controls were excluded during data analysis. Our data were therefore analysed on one hundred and nine (109) women (Fig. 1 ). Socio-demographic characteristics of participants Table I below summarizes the socio-demographic characteristics of the participants in our study. The mean age was 45 ± 11 years for breast cancer patients, 50 ± 11 years for cervical cancer patients and 46 ± 10 years for controls. The three average ages were compared using Kruskal-Wallis rank sum test and we found no statistically significant difference between these ages (p = 0.094; 95%CI). The majority of participants were married and had a secondary level of education, were unemployed, worked as housekippers and lived in Douala. Table 1 Socio-demographic characteristics of participants. Parameters Overall (N = 109) Breast cancer ( N = 29) Cervical cancer (N = 27) control (N = 53) P-value Age 47 ± 11 45 ± 11 50 ± 11 46 ± 10 0.094 Marital status 0.12 Married 50% (55/109) 52% (15/29) 37% (10/27) 57% (30/53) Celibate 42% (46/109) 34% (10/29) 52% (14/27) 42% (22/53) Widow 4.6% (5/109) 6.9% (2/29) 7.4% (2/27) 1.9% (1/53) Separate 1.8% (2/109) 6.9% (2/29) 0% (0/27) 0% (0/53) Divorce 0.9% (1/109) 0% (0/29) 3.7% (1/27) 0% (0/53) Level of study 0.059 Secondary 45% (49/109) 66% (19/29) 37% (10/27) 38% (20/53) Higher 38% (41/109) 17% (5/29) 41% (11/27) 47% (25/53) Primary 17% (19/109) 17% (5/29) 22% (6/27) 15% (8/53) Sector Of Activity 0.3 Jobless 44% (48/109) 31% (9/29) 59% (16/27) 43% (23/53) Informal 37% (40/109) 48% (14/29) 30% (8/27) 34% (18/53) Formal 19% (21/109) 21% (6/29) 11% (3/27) 23% (12/53) Profession na Housekeeper 30% (33/109) 21% (6/29) 33% (9/27) 34% (18/53) Saleswoman 19% (21/109) 31% (9/29) 11% (3/27) 17% (9/53) Student 13% (14/109) 10% (3/29) 22% (6/27) 9.4% (5/53) Teacher 8.3% (9/109) 3.4% (1/29) 7.4% (2/27) 11% (6/53) Hotels 4.6% (5/109) 10% (3/29) 0% (0/27) 3.8% (2/53) Accountant 3.7% (4/109) 0% (0/29) 3.7% (1/27) 5.7% (3/53) Couturer 3.7% (4/109) 0% (0/29) 0% (0/27) 7.5% (4/53) Hairdresser 2.8% (3/109) 3.4% (1/29) 3.7% (1/27) 1.9% (1/53) Nurse 2.8% (3/109) 3.4% (1/29) 0% (0/27) 3.8% (2/53) Secretary 1.8% (2/109) 0% (0/29) 3.7% (1/27) 1.9% (1/53) Executive Assistant 0.9% (1/109) 3.4% (1/29) 0% (0/27) 0% (0/53) Atms 0.9% (1/109) 0% (0/29) 0% (0/27) 1.9% (1/53) Call Box 0.9% (1/109) 0% (0/29) 3.7% (1/27) 0% (0/53) Communicator 0.9% (1/109) 0% (0/29) 0% (0/27) 1.9% (1/53) Cultivator 0.9% (1/109) 0% (0/29) 3.7% (1/27) 0% (0/53) Gendarme 0.9% (1/109) 3.4% (1/29) 0% (0/27) 0% (0/53) Institute 0.9% (1/109) 0% (0/29) 3.7% (1/27) 0% (0/53) Pharmacist 0.9% (1/109) 3.4% (1/29) 0% (0/27) 0% (0/53) Plantation Evecam 0.9% (1/109) 3.4% (1/29) 0% (0/27) 0% (0/53) Stylist 0.9% (1/109) 0% (0/29) 3.7% (1/27) 0% (0/53) Surface Technician 0.9% (1/109) 3.4% (1/29) 0% (0/27) 0% (0/53) Town Of Residence 0.03* Douala 54% (59/109) 52% (15/29) 70% (19/27) 47% (25/53) Yaounde 9.2% (10/109) 17% (5/29) 0% (0/27) 9.4% (5/53) Bamenda 4.6% (5/109) 10% (3/29) 7.4% (2/27) 0% (0/53) Bafang 2.8% (3/109) 0% (0/29) 3.7% (1/27) 3.8% (2/53) Buea 2.8% (3/109) 6.9% (2/29) 0% (0/27) 1.9% (1/53) Dschang 2.8% (3/109) 0% (0/29) 3.7% (1/27) 3.8% (2/53) Bafoussam 1.8% (2/109) 3.4% (1/29) 0% (0/27) 1.9% (1/53) Kribi 1.8% (2/109) 3.4% (1/29) 3.7% (1/27) 0% (0/53) Kumba 1.8% (2/109) 0% (0/29) 0% (0/27) 3.8% (2/53) Mbouda 1.8% (2/109) 3.4% (1/29) 0% (0/27) 1.9% (1/53) North 1.8% (2/109) 0% (0/29) 0% (0/27) 3.8% (2/53) Tiko 1.8% (2/109) 0% (0/29) 0% (0/27) 3.8% (2/53) Bagangte 0.9% (1/109) 0% (0/29) 0% (0/27) 1.9% (1/53) Bawoung 0.9% (1/109) 0% (0/29) 3.7% (1/27) 0% (0/53) Edea 0.9% (1/109) 0% (0/29) 3.7% (1/27) 0% (0/53) Foumbam 0.9% (1/109) 0% (0/29) 0% (0/27) 1.9% (1/53) Garoua 0.9% (1/109) 0% (0/29) 0% (0/27) 1.9% (1/53) Kousseri 0.9% (1/109) 0% (0/29) 0% (0/27) 1.9% (1/53) Libreville 0.9% (1/109) 0% (0/29) 0% (0/27) 1.9% (1/53) Limbe 0.9% (1/109) 0% (0/29) 0% (0/27) 1.9% (1/53) Mebealem 0.9% (1/109) 0% (0/29) 0% (0/27) 1.9% (1/53) Moyoka 0.9% (1/109) 0% (0/29) 3.7% (1/27) 0% (0/53) Moyoko 0.9% (1/109) 0% (0/29) 0% (0/27) 1.9% (1/53) Nkongsamba 0.9% (1/109) 3.4% (1/29) 0% (0/27) 0% (0/53) Penja 0.9% (1/109) 0% (0/29) 0% (0/27) 1.9% (1/53) Pinyin 0.9% (1/109) 0% (0/29) 0% (0/27) 1.9% (1/53) Continuous data were presented in the form of mean and standard deviation(Mean ± SD). Categorical data were presented in the form of percentage and frequency(% (n/N)). P-value: continuous data (Kruskal-Wallis rank sum test); categorial data (Fisher’s exact test). Clinical characteristics of patients Table II describes the clinical characteristics of the patients. In both groups, the most represented numbers of treatments were respectively 3 (16 patients or 28.5%), 4 (11 patients or 19.5%), 6 (9 patients or 16.1%), 5 (5 patients or 8.9%). The most represented stage was stage I (34 patients or 60.7%) followed by stage II (18 patients or 32.1%). Stages 0 and III were the least represented, respectively 1 and 3 patients for a cumulative percentage of 7.2%. (Table 2 ) Table 2 Clinical characteristics of patients Breast cancer (n = 29) Cervical cancer (n = 27) Total (n = 56) Variables N % n % n % number of chemotherapy 1 3 10,3 0 0.0 3 5.4 2 2 6.9 1 3.7 3 5.4 3 8 27.7 8 29.6 16 28.5 4 3 10.3 8 29.6 11 19.5 5 3 10.3 2 7.4 5 8.9 6 4 13.9 5 18.6 9 16.1 7 0 0.0 2 7.4 2 3.6 8 2 6.9 0 0.0 2 3.6 9 2 6.9 0 0.0 2 3.6 10 1 3.4 0 0.0 1 1.8 11 1 3.4 0 0.0 1 1.8 31 0 0.0 1 3.7 1 1.8 Stage of cancer 0 0 0.0 1 3.7 1 1.8 1 19 65.5 15 55.6 34 60.7 2 8 27.6 10 37.0 18 32.1 3 2 6.9 1 3.7 3 5.4 Body composition and metabolic profile in cases and controls Table 3 is a comparative analysis of body composition parameters (muscle mass, body fat percentage and body water percentage) and metabolic profile (creatinine and albumin concentrations) between cases and controls. The muscle mass, body fat and body water percentages of breast and cervical cancer patients undergoing chemotherapy were significantly different from those of controls. Creatinine and albumin concentrations in breast and cervical cancer patients undergoing chemotherapy are also different from controls. Table 3 muscle mass, body fat percentage, body water percentage, creatinine and albumin concentration in cases and controls Participants Parameters Breast cancer (n = 29) Cervical cancer (n = 27) Controls (n = 53) Muscle mass (Kg) 39,65 ± 7,07 b 38,18 ± 4,87 b 44,73 ± 8,12 a Body fat percentage (%) 37,47 ± 9,70 b 37,55 ± 6,78 b 42,33 ± 5,86 a Albumin (g/dl) 3,49 ± 0,89 b 3,85 ± 1,14 b 4,55 ± 0,69 a Creatinine (mg/dl) 0,90 ± 0,33 b 0,93 ± 0,38 b 0,55 ± 0,15 a Body water percentage (%) 37,40 ± 6,56 b 38,45 ± 6,23 ab 40,96 ± 4,28 a Data are presented as mean ± standard deviation (SD); ordered analysis of variance and Duncan's post hoc test were used to make comparisons. For the same line, figures bearing the same letter are not statistically significant at P < 0.05 Variation in body composition and metabolic profile between stages 1 and 2 of the disease in patients with breast or cervical cancer Figures 2 and 3 describe the changes in muscle mass, percentage of body fat and body water (Fig. 2) followed by creatinine and albumin concentrations (Fig. 3 ) between stage 1 and stage 2 of the disease in patients with breast and cervical cancer. The results were that muscle mass decreased significantly between stage 1 and stage 2 in patients with breast cancer (p = 0.001) but not in patients with cervical cancer (p = 0.91). Body fat percentages increased in breast cancer patients between stage 1 and stage 2 but not significantly (p = 0.37) and decreased between the two stages in cervical cancer patients but not significantly (p = 0.45). Body water percentages also decreased between stage 1 and stage 2 of the disease, but not significantly in breast cancer patients (p = 0.35) and cervical cancer patients (p = 0.15). In terms of metabolic profile, creatinine concentrations increased between stage 1 and stage 2 but not significantly in breast cancer patients (p = 0.09) and cervical cancer patients (p = 0.76). Albumin concentrations increased between stage 1 and stage 2 in breast cancer patients but not significantly (p = 0.54). in patients with cervical cancer, albumin concentrations decreased significantly between stage 1 and stage 2 (p = 0.01). DISCUSSION Breast and cervical cancers are the deadliest cancers among women in the world and particularly in Cameroon. In this country where the therapeutic index of anticancer treatments remains low resulting in high cancer mortality rates, we set out to contribute to scientific knowledge on the impact of changes in body composition on the response to chemotherapy of breast and cervical cancers. We conducted a case control study so the general objective was to determine the impact of chemotherapy and stage of disease on changes in body composition in women with breast or cervical cancer followed at the oncology unit of Douala General Hospital. On the socio-demographic aspect. The mean age of patients with breast cancer was 45 ± 11 years, that of patients with cervical cancer was 50 ± 11 years. And the mean age of controls was 46 ± 10 years old. These mean ages are not statistically different (p = 0.09) because we matched the ages of the cases to those of the controls to better study the changes in body composition. However, these mean ages remain approximately equal to those found in previous studies carried out within the same service ( 28 ) and those found in the majority of other regions across the country( 29 ). During chemotherapy, younger patients tolerate side effects better compared to older patients. Indeed, age is an important parameter which contributes considerably to changes in body composition. Roberto Buffa and al in 2011 found that increasing age contributed to a loss of muscle mass, body water and a gain of body fat ( 30 ). But tumor progression and chemotherapy can significantly contribute to alterations in body composition regardless of age( 3 – 5 ). Measurement of body composition by bioimpedancemetry in patients with Breast or cervical cancer undergoing chemotherapy revealed that muscle masses decreased in both groups compared to controls and between stage 1 to stage2. These results are similar to those found Rier and al in 2018( 31 ). The biological mechanisms that may explain these results are induction by tumor progression or therapeutic molecules of loss of muscle metabolic and structural proteins, mitochondrial dysfunction, altered oxidative phosphorylation in the tricarboxylic acid cycle, calcium signaling and fatty acid metabolism( 32 ). Others mechanisms like explained these results by the fact that muscle proteins are usually broken down in the host to produce energy and support the mechanisms of angiogenesis and tumour progression( 33 ). Several authors point to inflammation as the reason for the high levels of inflammatory markers in cancer ( 34 , 35 ). Indeed, it has been shown that elevated levels of C-reactive protein, a fibrogen in cancer, affect both muscle protein degradation and synthesis through several signalling pathways. Further molecular analysis shows activation of the muscle atrophy genes antrogin-1 and MuRF-1( 36 ) as evidenced by elevated mRNA levels of these genes in cancer ( 37 ). We found that the percentage of body fat was significantly lower in breast and cervical cancer patients than in controls, and that it decreased but not significantly between stage 1 and stage 2 of the disease. Ginzal and al , Halpem-silveira and al in 2020 in a prospective study found a loss of adipose tissue during chemotherapy( 38 , 39 ). On the other hand, in a recently published meta-analysis, the authors found that there was a gain in body fat during chemotherapy( 40 ). Certain cyclophosphamide-based chemotherapy protocols are implicated in fat mass gain. Analysis of body water percentages showed that they decreased significantly in patients compared with controls and between stage 1 and stage 2, but not significantly. this result shows that patients are not sufficiently hydrated during chemotherapy and yet water is strongly involved in drug clearance during chemotherapy. Measurements of serum creatinine concentrations revealed that the concentration of this protein was very high in cases compared to controls, but remained unchanged during stage 1 and stage 2. Our observations are close to those found by Chauhan et al (2016) who found in their study that mean creatinine values measured during treatment in cancer patients remained within the normal range (0.6–1.1 g/dl). He also found that mean creatinine values were 1.05 ± 0.59 g/dl and remained unchanged during treatment ( 41 ). These results corroborate those found by Olubumni and al (2018) who found a significant increase (p = 0.02) in serum creatinine levels in both cancer types compared to controls( 42 ). These authors explain these results by the fact that cancer cells draw their energy partly from the muscle to divide. To this end, a rapid ATP production system is set up while waiting for the metabolic pathways to adapt to the increased demand for ATP. ADP then couples with creatine phosphate (creatine kinase) during the hydrolysis of creatine to creatinine (a high energy compound stored in the muscles). This results in an increase in the concentration of creatinine in the body. In our work we found that serum albumin concentrations decreased in cervical cancer patients during chemotherapy (from 4.33 ± 1.12 g/dl in stage 1 to 3.15 ± 0.93 g/dl in stage 2; p = 0.01). In contrast, this decrease was insignificant in breast cancer patients (from 3.60 ± 0.86 g/dl in stage 1 to 3.37 ± 0.68 in stage 2; p = 0.55). These results corroborate those of Yadav et al (2016) who found a reduction in albumin levels during chemotherapy( 41 ). They explain these results by the fact that the anorexia observed in the cancer situation contributes to the reduction of food intake and consequently to the decrease in albumin levels observed in our patients. Albumin, in addition to being a marker of muscle metabolism, has antioxidant and anti-inflammatory properties ( 43 ). The reduction in albumin levels during chemotherapy in both types of cancer may be explained by increased production of reactive oxygen species and free radicals( 43 ). LIMITATIONS This work was inspired by my therapeutic follow-up sessions with these patients. We noted some limitations during the study such as the fact that we worked at the early stages of the disease. We intend to explore in a future study the changes in body composition from stage 1 to stage 4 of the disease in order to better appreciate these changes and take related measures. CONCLUSION The components of body composition assessed during our study (muscle mass, percentage of body fat and body water) associated with the metabolic profile (creatinine and albumin concentrations) decreased significantly in early-stage breast and cervical cancer patients undergoing chemotherapy compared with cancer-free women not undergoing chemotherapy, and non-significantly between stage 1 and stage 2 of the disease. Chemotherapy and tumour progression therefore had a negative impact on changes in body composition. Declarations Data availability The data used to support the results of this study are available from the corresponding author on reasonable request. Conflicts of interest The authors declare no conflicts of interest. Authors' contributions DA, EDB and WSNM conceived the idea and the study. DA, JCM, AMMM and WSNM collected and enered the data in the field. PN and EDB supervised data collection in the hospitals. Author DA coordinated data entry, WSNM created figures, performed statistical analyses and interpreted the results with the help of DA. DA drafted the first version of the manuscript with the help of WSNM. Authors EDB, PN, JCM and AMMM reviewed the paper for important intellectual content. Authors EDB and PN supervised the work at all stages. All authors read and approved the final document before submission. ACKNOWLEDGMENTS The authors thank the women who agreed to participate in the study. FUNDING DECLARATION we declare that we have received no funding for this work References Shachar SS, Deal AM, Weinberg M, Williams GR, Nyrop KA, Popuri K, et al. Body Composition as a Predictor of Toxicity in Patients Receiving Anthracycline and Taxane–Based Chemotherapy for Early-Stage Breast Cancer. Clin Cancer Res. 2017;23(14):3537–43. Cespedes Feliciano EM, Chen WY, Lee V, Albers KB, Prado CM, Alexeeff S, et al. 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Miyamoto Y, Baba Y, Sakamoto Y, Ohuchi M, Tokunaga R, Kurashige J, et al. Negative Impact of Skeletal Muscle Loss after Systemic Chemotherapy in Patients with Unresectable Colorectal Cancer. PLoS ONE. 2015;10(6):e0129742. Jung HW, Kim JW, Kim JY, Kim SW, Yang HK, Lee JW, et al. Effect of muscle mass on toxicity and survival in patients with colon cancer undergoing adjuvant chemotherapy. Support Care Cancer. 2015;23(3):687–94. Zou Z, Chang H, Li H, Wang S. Induction of reactive oxygen species: an emerging approach for cancer therapy. Apoptosis. 2017;22(11):1321–35. Barbosa S, Pedrosa MB, Ferreira R, Moreira-Gonçalves D, Santos LL. The impact of chemotherapy on adipose tissue remodeling: The molecular players involved in this tissue wasting. Biochimie. 2024;223:1–12. Suzuki K, Furuse H, Tsuda T, Masaki Y, Okazawa S, Kambara K, et al. Utility of creatinine/cystatin C ratio as a predictive marker for adverse effects of chemotherapy in lung cancer: A retrospective study. J Int Med Res. 2015;43(4):573–82. Kawai K, Hinotsu S, Tomobe M, Akaza H. Serum Creatinine Level During Chemotherapy for Testicular Cancer as a Possible Predictor of Bleomycin-induced Pulmonary Toxicity. Jpn J Clin Oncol. 1998;28(9):546–50. Yang M, Zhang Q, Ruan GT, Tang M, Zhang X, Song MM et al. Association Between Serum Creatinine Concentrations and Overall Survival in Patients With Colorectal Cancer: A Multi-Center Cohort Study. Front Oncol [Internet]. 2021 Oct 7 [cited 2024 May 12];11. https://www.frontiersin.org/journals/oncology/articles/ 10.3389/fonc.2021.710423/full . Lis C, Grutsch J, Vashi P, Lammersfeld C. Is serum albumin an independent predictor of survival in patients with breast cancer? J Parenter Enter Nutr. 2003;27(1):10–5. Oñate-Ocaña LF, Aiello-Crocifoglio V, Gallardo-Rincón D, Herrera-Goepfert R, Brom-Valladares R, Carrillo JF, et al. Serum Albumin as a Significant Prognostic Factor for Patients with Gastric Carcinoma. Ann Surg Oncol. 2007;14(2):381–9. cancer Statistic in Cameroon 2020 GLOBOCAN.pdf. Grad FP. The Preamble of the Constitution of the World Health Organization. Janssen I, Heymsfield SB, Wang Z, Ross R. Skeletal muscle mass and distribution in 468 men and women aged 18–88 year. J Appl Physiol. 2000;89(1):81–8. Lintsi M, Kaarma H, Kull I. Comparison of hand-to-hand bioimpedance and anthropometry equations versus dual-energy X-ray absorptiometry for the assessment of body fat percentage in 17–18-year-old conscripts. Clin Physiol Funct Imaging. 2004;24(2):85–90. Chumlea WC, Guo SS, Zeller CM, Reo NV, Baumgartner RN, Garry PJ, et al. Total body water reference values and prediction equations for adults. Kidney Int. 2001;59(6):2250–8. Husdan H, Rapoport A. Estimation of Creatinine by the Jaffe Reaction: A Comparison of Three Methods. Clin Chem. 1968;14(3):222–38. Mashiba S, Uchida K, Okuda S, Tomita S. Measurement of glycated albumin by the nitroblue tetrazolium colorimetric method. Clin Chim Acta. 1992;212(1):3–15. Lemouchele IN, Mbougang SP, Bell ED, Ebongue CO, Foko LPK, Enyegue ELE, et al. Breast Cancer among Young Women in Douala, Cameroon: Epidemiological, Clinical, Behavioural Characteristics and Risk Factors. J Cancer Tumor Int. 2022;12(2):23–38. Engbang JP, Essome H, Koh VM, Simo G, Essam JD, Mouelle AS, et al. Breast cancer in Cameroon, histo-epidemiological profile: about 3044 cases. Pan Afr Med J. 2015;21:242–242. Buffa R, Floris GU, Putzu PF, Marini E. Body Composition Variations in Ageing. Coll Antropol. 2011. Rier HN, Jager A, Sleijfer S, van Rosmalen J, Kock MCJM, Levin MD. Changes in body composition and muscle attenuation during taxane-based chemotherapy in patients with metastatic breast cancer. Breast Cancer Res Treat. 2018;168(1):95–105. Barreto R, Mandili G, Witzmann FA, Novelli F, Zimmers TA, Bonetto A. Cancer and Chemotherapy Contribute to Muscle Loss by Activating Common Signaling Pathways. Front Physiol [Internet]. 2016 Oct 19 [cited 2024 May 19];7. https://www.frontiersin.org/journals/physiology/articles/ 10.3389/fphys.2016.00472/full . Wallengren O, Iresjö BM, Lundholm K, Bosaeus I. Loss of muscle mass in the end of life in patients with advanced cancer. Support Care Cancer. 2015;23(1):79–86. Neefjes ECW, van den Hurk RM, Blauwhoff-Buskermolen S, van der Vorst MJDL, Becker-Commissaris A, de van der Schueren MAE, et al. Muscle mass as a target to reduce fatigue in patients with advanced cancer. J Cachexia Sarcopenia Muscle. 2017;8(4):623–9. Dalle S, Rossmeislova L, Koppo K. The Role of Inflammation in Age-Related Sarcopenia. Front Physiol [Internet]. 2017 Dec 12 [cited 2024 May 19];8. https://www.frontiersin.org/journals/physiology/articles/ 10.3389/fphys.2017.01045/full . Fanzani A, Conraads VM, Penna F, Martinet W. Molecular and cellular mechanisms of skeletal muscle atrophy: an update. J Cachexia Sarcopenia Muscle. 2012;3(3):163–79. Al Sarakbi W, Sasi W, Jiang W, Roberts T, Newbold R, Mokbel K. The mRNA expression of SETD2 in human breast cancer: correlation with clinico-pathological parameters. BMC Cancer. 2009;9(1):290. Ginzac A, Barres B, Chanchou M, Gadéa E, Molnar I, Merlin C, et al. A decrease in brown adipose tissue activity is associated with weight gain during chemotherapy in early breast cancer patients. BMC Cancer. 2020;20(1):96. Halpern-Silveira D, Susin LRO, Borges LR, Paiva SI, Assunção MCF, Gonzalez MC. Body weight and fat-free mass changes in a cohort of patients receiving chemotherapy. Support Care Cancer. 2010;18(5):617–25. van den Berg MMGA, Winkels RM, de Kruif JTCM, van Laarhoven HWM, Visser M, de Vries JHM, et al. Weight change during chemotherapy in breast cancer patients: a meta-analysis. BMC Cancer. 2017;17(1):259. Chauhan P, Yadav R, Kaushal V, Beniwal P. Evaluation of serum biochemical profile of breast cancer patients. Int J Med Res Health Sci. 2016;5:1–7. Ojo OC, Asaolu MF, Oyeyemi AO, Akinlua I, Molehin OM, Oyebanji OG. STATUS OF PLASMA ELECTROLYTES, UREA, CREATININE, AND C-REACTIVE PROTEIN IN CANCER PATIENTS. Asian J Pharm Clin Res. 2018;11(1):268. Roche M, Rondeau P, Singh NR, Tarnus E, Bourdon E. The antioxidant properties of serum albumin. FEBS Lett. 2008;582(13):1783–7. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4564004","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":314291188,"identity":"b0f3b86f-35bc-4bd8-8546-8146589926b3","order_by":0,"name":"Dominique Anaba","email":"","orcid":"","institution":"Douala General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dominique","middleName":"","lastName":"Anaba","suffix":""},{"id":314291189,"identity":"a93b2cc9-ecf8-4d5a-9e7f-938209764b9e","order_by":1,"name":"Wilfried Steve Ndeme Mboussi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYFCCBBiD+QCQkJAhVosBELOBWBI8pGjhAREMhLWYsycf3fDhzx85g+M9n1/dqLHgYWA/fHQDPi2WPc/Sbs5sMzA2OHN2m3XOMaDDeNLSbuDTYnAjx+w2b4NB4swZuduMc9iAWiR4zAhoyf92+88fg/qZ8988M875R5SWHLbbDGwGCfwSPMyPc9uI0XLmmdnN3jZjw36eNDPm3D4JHjaCfjme/OzGjz9y8mzshx9/zvlWJ8fPfvgYXi3IgE0CTBKrHASYP5CiehSMglEwCkYOAABQlEnm3E2BRgAAAABJRU5ErkJggg==","orcid":"","institution":"The University of Douala","correspondingAuthor":true,"prefix":"","firstName":"Wilfried","middleName":"Steve Ndeme","lastName":"Mboussi","suffix":""},{"id":314291190,"identity":"cbbfe416-f6f7-4ccf-99b1-c50ed9dfe38e","order_by":2,"name":"Ester Dina Bell","email":"","orcid":"","institution":"Douala General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ester","middleName":"Dina","lastName":"Bell","suffix":""},{"id":314291191,"identity":"47468ec7-e1f7-4399-bd21-9a7811242a7e","order_by":3,"name":"Anne Marthe Maison Mayeh","email":"","orcid":"","institution":"Douala General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Anne","middleName":"Marthe Maison","lastName":"Mayeh","suffix":""},{"id":314291192,"identity":"f9402166-b55b-419b-b40e-7c5db501e813","order_by":4,"name":"Jean Charles Mananga","email":"","orcid":"","institution":"Douala General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jean","middleName":"Charles","lastName":"Mananga","suffix":""},{"id":314291193,"identity":"52aca7e0-94eb-4ef0-aad2-c51ac789c3bb","order_by":5,"name":"Paul Ndom","email":"","orcid":"","institution":"University of Yaounde","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Ndom","suffix":""}],"badges":[],"createdAt":"2024-06-11 12:14:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4564004/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4564004/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60185214,"identity":"acf60d4e-39c3-4bdc-a36e-49f705c57fe4","added_by":"auto","created_at":"2024-07-12 18:40:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003erecruitment diagram for patients included in our study\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4564004/v1/50144e2d931f269c5f8709f4.png"},{"id":60185215,"identity":"1bc31f0b-d2d7-4183-965e-94c041c7d0c3","added_by":"auto","created_at":"2024-07-12 18:40:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":152600,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in muscle mass, percentage of body fat and body water between stage 1 and stage 2 disease in patients with breast and cervical cancer\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4564004/v1/ecd7c2010245b998bba4e4e1.png"},{"id":60185213,"identity":"d66dfd44-ad7e-4e9f-b989-5b70255ba470","added_by":"auto","created_at":"2024-07-12 18:40:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60296,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in percentage creatinine and albumin concentrations between stage 1 and stage 2 disease in patients with breast and cervical cancer\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4564004/v1/7569721b22fd39c56c5b0b51.png"},{"id":67767328,"identity":"3a84a852-7558-4e6c-bcd3-2ad7443bb739","added_by":"auto","created_at":"2024-10-29 13:24:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1202159,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4564004/v1/bad673fd-34bf-43d9-8301-b0900a2494e2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Body composition and metabolic profile during chemotherapy in early-stage breast and cervical cancer patients in Douala, Cameroon: A hospital-based study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003echanges in body composition during chemotherapy have an impact on patients' vital prognosis, as several studies have shown (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The biochemical and anatomical model of body composition as described by Kaffel et al in 2021 demonstrates a model for analysing body composition, the components of which, notably adipose, muscle and water, are of vital importance during chemotherapy(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBody water volume during chemotherapy is of vital importance in the elimination of therapeutic molecules and the maintenance of optimal renal clearance(\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), such as muscle mass, which has been shown to help regulate the immune system, reduce the side-effects of chemotherapy, and have a positive influence on vital prognosis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Adipose tissue has been described as a poor marker of vital prognosis during chemotherapy, given its involvement in the production of reactive oxygen species and drug toxicity(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). These changes in body composition during chemotherapy are not only physical but also metabolic. Certain biomarkers of muscle metabolism such as creatinine, vitamin D, leptin and albumin could be involved. Study of creatinine clearance has demonstrated its value in cancer chemotherapy. It has been described as a predictive biomarker of the side effects of chemotherapy (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), of the toxicity of certain therapeutic molecules (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and as a marker of overall survival (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Albumin is also used as a prognostic and survival factor during chemotherapy(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Chemotherapy, whether adjuvant or neoadjuvant, is the most widely used cancer treatment in Cameroon, particularly for breast and cervical cancer. These cancers are the most deadly among women worldwide, but also in Cameroon, where they caused 2,108 and 1,787 deaths respectively in 2020 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The treatment of these cancers in Cameroon is characterised by a very low therapeutic index and the majority of patients who start therapy die 12 months after the start of therapy. Knowing the impact of changes in body composition on the response to chemotherapy, it is important for us to investigate its profile in patients undergoing chemotherapy. The general objective of this study was to determine the impact of chemotherapy and stage of disease on changes in body composition in women with breast or cervical cancer followed at the oncology unit of Douala General Hospital.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy site\u003c/h2\u003e \u003cp\u003eThe study was conducted from November 2023 to April 2024 at the oncology department of Douala General Hospital (4\u0026deg; 03\u0026rsquo; 55\u0026rsquo;\u0026rsquo;NORD, 9\u0026deg; 45\u0026rsquo; 31\u0026rsquo;\u0026rsquo;EST), Littoral region, Wouri division, Cameroon. Douala General Hospital is one of five first-class hospitals in Cameroon. The hospital has a myriad of general and specialist medical services, including an oncology department. The latter comprises three units specialized in the diagnosis and treatment of cancer, including surgery, radiotherapy, and chemotherapy. As the treating oncologist at this hospital, our research team chose it as the place to study because of the large number of patients we receive from all over Cameroon and Central Africa.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe study included three groups of women. The first two groups consisted of women with breast or cervical cancer whose histological and biological diagnosis had been confirmed by me or by one of the colleagues in the department undergoing chemotherapy, or who had been transferred by other oncologists in the country or in the Central African sub-region for follow-up in our hospital. The third group of women was made up of women with no type of cancer or with no clinical or symptomatological signs of a disease recurrent within the hospital, who could be nurses, care assistants or sick call nurses who had given their informed consent to take part in the study. the other women, whether ill or not, who refused to take part in the study by giving their informed consent were not included in the study, and this in no way changed the quality of the service provided. The sample size was defined by convincing. We included one hundred and nine women in our study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eTo achieve our objective, we conducted a case-control study to better assess the impact of chemotherapy on body composition. Cases recruited as women newly diagnosed with breast cancer or cervical cancer having freely and informedly agreed to participate in our study; controls defined as women without cancer and meeting the criteria of good health defined according to the WHO (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), recruited within the said hospitals (nurses, care assistants, nursing staff).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eA structured questionnaire was used to collect sociodemographic, clinical, body composition, creatinine and albumin concentrations data from the patients after signing the informed consent. The questionnaire was completed after explaining the purpose of the study during a 10\u0026ndash;15 minutes interview with the patient. The first part was designed to collect the patients' sociodemographics informations and body composition parameters, including marital status, level of study, sector of activity, profession, town of residence, age, muscle mass, body fat and body water percentage. The second part collected clinical and therapeutics informations on the disease (type of cancer, stage, presence or absence of metastases, treatment protocol, and dose of the drug used). The final part collected the information about creatinine and albumin concentration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e- Measuring the components of body composition\u003c/h2\u003e \u003cp\u003eMuscle mass, body fat an body water percentages have been measured by bioimpedance analysis (BIA) using a calibrated system of equations by DXA (Dual-energy X-ray absorptiometry) to calculate muscle mass as proposed by Janssen \u003cem\u003eet al\u003c/em\u003e (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), body fat percentage (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) and body water percentage (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e- Measuring of creatinine and albumin concentrations\u003c/h2\u003e \u003cp\u003eFour milliliters (04 ml) of blood was collected from each patient by venipuncture using Eclipse safety collection needles with port-tube in sterile dry tubes that were labeled and stored in an appropriate cooler. The blood samples were then transported the same day to laboratory for biological analysis, then centrifuged at 3000 rpm. The sera obtained were transferred into Eppendorf\u0026rsquo;s tubes for creatinine and albumin determination. Creatinine measurement was performed according to the method described by Jaff\u0026eacute; (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). And albumin was measured using the colorimetric method (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eEthical considerations\u003c/h2\u003e \u003cp\u003e This study was conducted according to the guidelines for clinical research on experimental models for clinical research on humans, as indicated by the Ministry of Public Health of Cameroon. Administrative authorizations were issued by the institutional human health research ethics committee of the University of Douala (N\u0026deg; 3050 CEI-Udo/04/2022/T) and the Douala General Hospital (N\u0026deg;458 AR/MINSANTE/HGD/DM/08/22). The aim and objectives of the study were explained to each participant in the language they understood best (French or English). Only participants who signed an informed consent form were admitted to the study. Participation in the study was voluntary, and women were free to refuse to answer all relevant questions and to withdraw from the study if they wished at any time. Also, there was no difference in management between women who agreed to take part in the study and those who did not. Each participant was made aware of the importance of practising physical activity during therapy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eData were entered into an Excel sheet (Microsoft Office, USA) and subsequently analysed with SPSS version 16 for Windows (SPSS, IBM, Chicago, IL, USA). The qualitative and quantitative variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and percentage, respectively. The one-way analysis of variance (ANOVA) was used to compare means and subsequently Duncan's post hoc test was used to make pairwise comparisons. The non-parametric Mann-Whitney test was used to make comparisons when the ANOVA could not be used. The Pearson correlation was used to study the relationship between the different parameters. The significance level was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSelection procedure for newly diagnosed cancer patients included in the study\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e120 women were recruited during the study period. 60 were women newly diagnosed with breast cancer (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) or cervical cancer (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) and 60 were control women recruited within the hospital. During data collection, one (01) breast cancer patient and three (03) cervical cancer patients could not be sampled due to anaemia. Among the female controls, seven (07) did not meet our matching criteria including age and body mass. Four (04) and seven (07) controls were excluded during data analysis. Our data were therefore analysed on one hundred and nine (109) women (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSocio-demographic characteristics of participants\u003c/h2\u003e \u003cp\u003eTable I below summarizes the socio-demographic characteristics of the participants in our study. The mean age was 45\u0026thinsp;\u0026plusmn;\u0026thinsp;11 years for breast cancer patients, 50\u0026thinsp;\u0026plusmn;\u0026thinsp;11 years for cervical cancer patients and 46\u0026thinsp;\u0026plusmn;\u0026thinsp;10 years for controls. The three average ages were compared using Kruskal-Wallis rank sum test and we found no statistically significant difference between these ages (p\u0026thinsp;=\u0026thinsp;0.094; 95%CI). The majority of participants were married and had a secondary level of education, were unemployed, worked as housekippers and lived in Douala.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic characteristics of participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall (N\u0026thinsp;=\u0026thinsp;109)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreast cancer ( N\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCervical cancer (N\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol (N\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50% (55/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52% (15/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37% (10/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57% (30/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCelibate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42% (46/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34% (10/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52% (14/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42% (22/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.6% (5/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9% (2/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.4% (2/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9% (1/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8% (2/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9% (2/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0% (0/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7% (1/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0% (0/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLevel of study\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45% (49/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66% (19/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37% (10/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38% (20/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38% (41/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17% (5/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41% (11/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47% (25/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17% (19/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17% (5/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22% (6/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15% (8/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSector Of Activity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJobless\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44% (48/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31% (9/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59% (16/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43% (23/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37% (40/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48% (14/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30% (8/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34% (18/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19% (21/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21% (6/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11% (3/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23% (12/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProfession\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ena\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousekeeper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30% (33/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21% (6/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33% (9/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34% (18/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaleswoman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19% (21/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31% (9/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11% (3/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17% (9/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13% (14/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10% (3/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22% (6/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.4% (5/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTeacher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.3% (9/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4% (1/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.4% (2/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11% (6/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHotels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.6% (5/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10% (3/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.8% (2/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccountant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.7% (4/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7% (1/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.7% (3/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCouturer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.7% (4/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.5% (4/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHairdresser\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8% (3/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4% (1/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7% (1/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9% (1/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8% (3/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4% (1/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.8% (2/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecretary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8% (2/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7% (1/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9% (1/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExecutive Assistant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4% (1/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0% (0/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9% (1/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCall Box\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7% (1/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0% (0/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunicator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9% (1/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultivator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7% (1/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0% (0/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGendarme\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4% (1/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0% (0/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7% (1/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0% (0/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePharmacist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4% (1/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0% (0/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlantation Evecam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4% (1/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0% (0/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStylist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7% (1/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0% (0/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurface Technician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4% (1/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0% (0/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTown Of Residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.03*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDouala\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54% (59/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52% (15/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70% (19/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47% (25/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYaounde\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.2% (10/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17% (5/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.4% (5/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBamenda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.6% (5/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10% (3/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.4% (2/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0% (0/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBafang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8% (3/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7% (1/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.8% (2/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8% (3/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9% (2/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9% (1/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDschang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8% (3/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7% (1/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.8% (2/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBafoussam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8% (2/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4% (1/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9% (1/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKribi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8% (2/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4% (1/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7% (1/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0% (0/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKumba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8% (2/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.8% (2/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMbouda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8% (2/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4% (1/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9% (1/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8% (2/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.8% (2/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTiko\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8% (2/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.8% (2/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBagangte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9% (1/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBawoung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7% (1/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0% (0/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7% (1/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0% (0/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoumbam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9% (1/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGaroua\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9% (1/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKousseri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9% (1/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLibreville\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9% (1/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimbe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9% (1/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMebealem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9% (1/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoyoka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7% (1/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0% (0/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoyoko\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9% (1/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNkongsamba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4% (1/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0% (0/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePenja\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9% (1/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePinyin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9% (1/109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0/29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9% (1/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eContinuous data were presented in the form of mean and standard deviation(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). Categorical data were presented in the form of percentage and frequency(% (n/N)).\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eP-value: continuous data (Kruskal-Wallis rank sum test); categorial data (Fisher\u0026rsquo;s exact test).\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics of patients\u003c/h2\u003e \u003cp\u003eTable II describes the clinical characteristics of the patients. In both groups, the most represented numbers of treatments were respectively 3 (16 patients or 28.5%), 4 (11 patients or 19.5%), 6 (9 patients or 16.1%), 5 (5 patients or 8.9%). The most represented stage was stage I (34 patients or 60.7%) followed by stage II (18 patients or 32.1%). Stages 0 and III were the least represented, respectively 1 and 3 patients for a cumulative percentage of 7.2%. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBreast cancer (n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eCervical cancer (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003enumber of chemotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage of cancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e60.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBody composition and metabolic profile in cases and controls\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e is a comparative analysis of body composition parameters (muscle mass, body fat percentage and body water percentage) and metabolic profile (creatinine and albumin concentrations) between cases and controls. The muscle mass, body fat and body water percentages of breast and cervical cancer patients undergoing chemotherapy were significantly different from those of controls. Creatinine and albumin concentrations in breast and cervical cancer patients undergoing chemotherapy are also different from controls.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003emuscle mass, body fat percentage, body water percentage, creatinine and albumin concentration in cases and controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eParticipants\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreast cancer\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCervical cancer\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle mass (Kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39,65\u0026thinsp;\u0026plusmn;\u0026thinsp;7,07\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38,18\u0026thinsp;\u0026plusmn;\u0026thinsp;4,87\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44,73\u0026thinsp;\u0026plusmn;\u0026thinsp;8,12\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody fat percentage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37,47\u0026thinsp;\u0026plusmn;\u0026thinsp;9,70\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37,55\u0026thinsp;\u0026plusmn;\u0026thinsp;6,78\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42,33\u0026thinsp;\u0026plusmn;\u0026thinsp;5,86\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,49\u0026thinsp;\u0026plusmn;\u0026thinsp;0,89\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,85\u0026thinsp;\u0026plusmn;\u0026thinsp;1,14\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,55\u0026thinsp;\u0026plusmn;\u0026thinsp;0,69\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,90\u0026thinsp;\u0026plusmn;\u0026thinsp;0,33\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,93\u0026thinsp;\u0026plusmn;\u0026thinsp;0,38\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,55\u0026thinsp;\u0026plusmn;\u0026thinsp;0,15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody water percentage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37,40\u0026thinsp;\u0026plusmn;\u0026thinsp;6,56\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38,45\u0026thinsp;\u0026plusmn;\u0026thinsp;6,23\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40,96\u0026thinsp;\u0026plusmn;\u0026thinsp;4,28\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD); ordered analysis of variance and Duncan's post hoc test were used to make comparisons. For the same line, figures bearing the same letter are not statistically significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eVariation in body composition and metabolic profile between stages 1 and 2 of the disease in patients with breast or cervical cancer\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigures 2 and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e describe the changes in muscle mass, percentage of body fat and body water (Fig.\u0026nbsp;2) followed by creatinine and albumin concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e) between stage 1 and stage 2 of the disease in patients with breast and cervical cancer. The results were that muscle mass decreased significantly between stage 1 and stage 2 in patients with breast cancer (p\u0026thinsp;=\u0026thinsp;0.001) but not in patients with cervical cancer (p\u0026thinsp;=\u0026thinsp;0.91). Body fat percentages increased in breast cancer patients between stage 1 and stage 2 but not significantly (p\u0026thinsp;=\u0026thinsp;0.37) and decreased between the two stages in cervical cancer patients but not significantly (p\u0026thinsp;=\u0026thinsp;0.45). Body water percentages also decreased between stage 1 and stage 2 of the disease, but not significantly in breast cancer patients (p\u0026thinsp;=\u0026thinsp;0.35) and cervical cancer patients (p\u0026thinsp;=\u0026thinsp;0.15).\u003c/p\u003e \u003cp\u003eIn terms of metabolic profile, creatinine concentrations increased between stage 1 and stage 2 but not significantly in breast cancer patients (p\u0026thinsp;=\u0026thinsp;0.09) and cervical cancer patients (p\u0026thinsp;=\u0026thinsp;0.76). Albumin concentrations increased between stage 1 and stage 2 in breast cancer patients but not significantly (p\u0026thinsp;=\u0026thinsp;0.54). in patients with cervical cancer, albumin concentrations decreased significantly between stage 1 and stage 2 (p\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eBreast and cervical cancers are the deadliest cancers among women in the world and particularly in Cameroon. In this country where the therapeutic index of anticancer treatments remains low resulting in high cancer mortality rates, we set out to contribute to scientific knowledge on the impact of changes in body composition on the response to chemotherapy of breast and cervical cancers. We conducted a case control study so the general objective was to determine the impact of chemotherapy and stage of disease on changes in body composition in women with breast or cervical cancer followed at the oncology unit of Douala General Hospital.\u003c/p\u003e \u003cp\u003eOn the socio-demographic aspect. The mean age of patients with breast cancer was 45\u0026thinsp;\u0026plusmn;\u0026thinsp;11 years, that of patients with cervical cancer was 50\u0026thinsp;\u0026plusmn;\u0026thinsp;11 years. And the mean age of controls was 46\u0026thinsp;\u0026plusmn;\u0026thinsp;10 years old. These mean ages are not statistically different (p\u0026thinsp;=\u0026thinsp;0.09) because we matched the ages of the cases to those of the controls to better study the changes in body composition. However, these mean ages remain approximately equal to those found in previous studies carried out within the same service (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) and those found in the majority of other regions across the country(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). During chemotherapy, younger patients tolerate side effects better compared to older patients. Indeed, age is an important parameter which contributes considerably to changes in body composition. Roberto Buffa and \u003cem\u003eal\u003c/em\u003e in 2011 found that increasing age contributed to a loss of muscle mass, body water and a gain of body fat (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). But tumor progression and chemotherapy can significantly contribute to alterations in body composition regardless of age(\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMeasurement of body composition by bioimpedancemetry in patients with Breast or cervical cancer undergoing chemotherapy revealed that muscle masses decreased in both groups compared to controls and between stage 1 to stage2. These results are similar to those found Rier and \u003cem\u003eal\u003c/em\u003e in 2018(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The biological mechanisms that may explain these results are induction by tumor progression or therapeutic molecules of loss of muscle metabolic and structural proteins, mitochondrial dysfunction, altered oxidative phosphorylation in the tricarboxylic acid cycle, calcium signaling and fatty acid metabolism(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Others mechanisms like explained these results by the fact that muscle proteins are usually broken down in the host to produce energy and support the mechanisms of angiogenesis and tumour progression(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Several authors point to inflammation as the reason for the high levels of inflammatory markers in cancer (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Indeed, it has been shown that elevated levels of C-reactive protein, a fibrogen in cancer, affect both muscle protein degradation and synthesis through several signalling pathways. Further molecular analysis shows activation of the muscle atrophy genes antrogin-1 and MuRF-1(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) as evidenced by elevated mRNA levels of these genes in cancer (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe found that the percentage of body fat was significantly lower in breast and cervical cancer patients than in controls, and that it decreased but not significantly between stage 1 and stage 2 of the disease. Ginzal and \u003cem\u003eal\u003c/em\u003e, Halpem-silveira and \u003cem\u003eal\u003c/em\u003e in 2020 in a prospective study found a loss of adipose tissue during chemotherapy(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). On the other hand, in a recently published meta-analysis, the authors found that there was a gain in body fat during chemotherapy(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Certain cyclophosphamide-based chemotherapy protocols are implicated in fat mass gain.\u003c/p\u003e \u003cp\u003eAnalysis of body water percentages showed that they decreased significantly in patients compared with controls and between stage 1 and stage 2, but not significantly. this result shows that patients are not sufficiently hydrated during chemotherapy and yet water is strongly involved in drug clearance during chemotherapy.\u003c/p\u003e \u003cp\u003eMeasurements of serum creatinine concentrations revealed that the concentration of this protein was very high in cases compared to controls, but remained unchanged during stage 1 and stage 2. Our observations are close to those found by Chauhan et al (2016) who found in their study that mean creatinine values measured during treatment in cancer patients remained within the normal range (0.6\u0026ndash;1.1 g/dl). He also found that mean creatinine values were 1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59 g/dl and remained unchanged during treatment (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). These results corroborate those found by Olubumni and al (2018) who found a significant increase (p\u0026thinsp;=\u0026thinsp;0.02) in serum creatinine levels in both cancer types compared to controls(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). These authors explain these results by the fact that cancer cells draw their energy partly from the muscle to divide. To this end, a rapid ATP production system is set up while waiting for the metabolic pathways to adapt to the increased demand for ATP. ADP then couples with creatine phosphate (creatine kinase) during the hydrolysis of creatine to creatinine (a high energy compound stored in the muscles). This results in an increase in the concentration of creatinine in the body.\u003c/p\u003e \u003cp\u003eIn our work we found that serum albumin concentrations decreased in cervical cancer patients during chemotherapy (from 4.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12 g/dl in stage 1 to 3.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93 g/dl in stage 2; p\u0026thinsp;=\u0026thinsp;0.01). In contrast, this decrease was insignificant in breast cancer patients (from 3.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86 g/dl in stage 1 to 3.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68 in stage 2; p\u0026thinsp;=\u0026thinsp;0.55). These results corroborate those of Yadav et al (2016) who found a reduction in albumin levels during chemotherapy(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). They explain these results by the fact that the anorexia observed in the cancer situation contributes to the reduction of food intake and consequently to the decrease in albumin levels observed in our patients. Albumin, in addition to being a marker of muscle metabolism, has antioxidant and anti-inflammatory properties (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). The reduction in albumin levels during chemotherapy in both types of cancer may be explained by increased production of reactive oxygen species and free radicals(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLIMITATIONS\u003c/h2\u003e \u003cp\u003eThis work was inspired by my therapeutic follow-up sessions with these patients. We noted some limitations during the study such as the fact that we worked at the early stages of the disease. We intend to explore in a future study the changes in body composition from stage 1 to stage 4 of the disease in order to better appreciate these changes and take related measures.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe components of body composition assessed during our study (muscle mass, percentage of body fat and body water) associated with the metabolic profile (creatinine and albumin concentrations) decreased significantly in early-stage breast and cervical cancer patients undergoing chemotherapy compared with cancer-free women not undergoing chemotherapy, and non-significantly between stage 1 and stage 2 of the disease. Chemotherapy and tumour progression therefore had a negative impact on changes in body composition.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used to support the results of this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDA, EDB and WSNM conceived the idea and the study. DA, \u0026nbsp; JCM, AMMM and WSNM collected and enered the data in the field. PN and EDB supervised data collection in the hospitals. Author DA coordinated data entry, WSNM created figures, performed statistical analyses and interpreted the results with the help of DA. DA drafted the first version of the manuscript with the help of WSNM. Authors EDB, PN, JCM and AMMM reviewed the paper for important intellectual content. Authors EDB and PN supervised the work at all stages. All authors read and approved the final document before submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the women who agreed to participate in the study. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING DECLARATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ewe declare that we have received no funding for this work\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShachar SS, Deal AM, Weinberg M, Williams GR, Nyrop KA, Popuri K, et al. 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BMC Cancer. 2017;17(1):259.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChauhan P, Yadav R, Kaushal V, Beniwal P. Evaluation of serum biochemical profile of breast cancer patients. Int J Med Res Health Sci. 2016;5:1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOjo OC, Asaolu MF, Oyeyemi AO, Akinlua I, Molehin OM, Oyebanji OG. STATUS OF PLASMA ELECTROLYTES, UREA, CREATININE, AND C-REACTIVE PROTEIN IN CANCER PATIENTS. Asian J Pharm Clin Res. 2018;11(1):268.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoche M, Rondeau P, Singh NR, Tarnus E, Bourdon E. The antioxidant properties of serum albumin. FEBS Lett. 2008;582(13):1783\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chemotherapy, Body composition, bioimpedancemetry, cancer","lastPublishedDoi":"10.21203/rs.3.rs-4564004/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4564004/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChanges in body composition during chemotherapy can negatively influence the prognosis of cancer patients. In order to assess changes in body composition in patients undergoing chemotherapy, a case-control study was conducted in the cobalt therapy departments of the Douala General Hospital. The overall objective of this study was to determine the impact of chemotherapy and stage of disease on changes in body composition in women with breast or cervical cancer followed at the oncology unit of Douala General Hospital. Muscle mass, body fat and body water percentages were measured by the bioimpedancemetry method and blood samples were collected for the measurement of albumin and creatinine concentrations. The results were analysed using SPSS version 16 for Windows (SPSS, IBM, Chicago, IL, USA). The mean age of the patients was 44.62\u0026thinsp;\u0026plusmn;\u0026thinsp;11.23 years for breast cancer (BC) patients, 50.37\u0026thinsp;\u0026plusmn;\u0026thinsp;10.78 years for cervical cancer (CC) patients and 46.11\u0026thinsp;\u0026plusmn;\u0026thinsp;10.43 years for controls. Muscle mass, body fat and body water decreased significantly in cases compared to controls (respectively p\u0026thinsp;=\u0026thinsp;0.0028, p\u0026thinsp;=\u0026thinsp;0.004, p\u0026thinsp;=\u0026thinsp;0.004). According to the stage of the disease when the two clinical groups were taken individually muscle mass decrease significantly between stage 1 to stage 2 in patients with BC (p\u0026thinsp;=\u0026thinsp;0.001), but not in patient with CC (p\u0026thinsp;=\u0026thinsp;0.84). Body fat and body water percentages decrease not significantly between stage 1 to stage 2 in the both cancer. Metabolically, creatinine concentrations were significantly elevated in both groups of patients compared with controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and albumin concentrations were significantly low (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In terms of disease stage, creatinine concentrations increased but not significantly between stage 1 and stage 2 in breast cancer patients (p\u0026thinsp;=\u0026thinsp;0.08) and decreased non-significantly in cervical cancer patients (p\u0026thinsp;=\u0026thinsp;0. 95). Albumin concentrations decreased significantly in cervical cancer patients (p\u0026thinsp;=\u0026thinsp;0.01) between stage 1 and stage 2 but did not decrease significantly in breast cancer patients (p\u0026thinsp;=\u0026thinsp;0.55). In conclusion, chemotherapy considerably altered the physical and metabolic body composition of breast and cervical cancer patients included in our study.\u003c/p\u003e","manuscriptTitle":"Body composition and metabolic profile during chemotherapy in early-stage breast and cervical cancer patients in Douala, Cameroon: A hospital-based study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-12 18:40:54","doi":"10.21203/rs.3.rs-4564004/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1aaacae1-6d78-4586-a1a5-d5e1ef67df8e","owner":[],"postedDate":"July 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-29T13:23:59+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-12 18:40:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4564004","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4564004","identity":"rs-4564004","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}