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This study aimed to determine the prognostic significance of low muscle volume (LMV) Bioelectrical Impedance Analysis (BIA), defined skeletal muscle index (SMI, Kg/m

male ≤8.75, female ≤5.75) in patients undergoing potentially curative surgery for Oesophageal Cancer (OC).

A prospective study of 122 patients diagnosed with OC [median age 65yr, 104 male, 65 neoadjuvant therapy] who underwent preoperative BIA (Maltron Bioscan 920). Primary outcome measure was Overall Survival (OS).

LMV was identified in 11 (9.0%) of patients, which was associated with low lean muscle mass (27.3 vs. 31.1kg, p=0.012), low body fat (8.8 vs.19.3kg, p<0.001), and greater total body water (72.2 vs. 62.2%, p=0.001), and more open & close laparotomies (36.4 vs. 8.1%, p=0.012). Median and 5-year OS was 16 months and 18.2% in LMV patients, compared with 51 months and 52.4% in non-sarcopenic patients (p=0.002). On multivariable analysis of pre-operative variables, only LMV (HR 2.75; 95% CI 1.33-5.66, p=0.006) was associated with OS.

BIA is an important prognostic indicator in OC and focused pre-habilitation consequently has strong potential.

BIA is an important prognostic indicator in OC and focused pre-habilitation consequently has strong potential.

Nutritional knowledge in patients with SARS-Cov2 infection (COVID-19) is limited. Our objectives were i) to assess malnutrition in hospitalized COVID-19 patients, ii) to investigate the links between malnutrition and disease severity at admission, iii) to study the impact of malnutrition on clinical outcomes such as transfer to an intensive care unit (ICU) or death.

Consecutive patients hospitalized in a medicine ward at a university hospital were included from March 21st to April 24th 2020 (n=114, 60.5% males, age 59.9±15.9 years). Nutritional status was defined using Global Leadership Initiative on Malnutrition (GLIM) criteria. Clinical, radiological and biological characteristics of COVID-19 patients were compared according to the presence of malnutrition. Logistic regression was used to assess associations between nutritional parameters and unfavourable outcomes such as transfer to intensive care unit (ICU) or death.

The overall prevalence of malnutrition was 42.1% (moderate 23.7%, severe 18.4%). The prevalence of malnutrition reached 66.7% in patients admitted from ICU. No significant association was found between nutritional status and clinical signs of COVID-19. Lower albumin levels were associated with a higher risk of transfer to ICU (for 10g/l of albumin, OR [95%CI] 0.31 [0.1; 0.7]; p<0.01) and this association was independent of age and CRP levels.

COVID-19 in medical units dedicated to non-intensive care is associated with a high prevalence of malnutrition, especially for patients transferred from ICU. These data emphasize the importance of early nutritional screening in these patients to adapt management accordingly.

COVID-19 in medical units dedicated to non-intensive care is associated with a high prevalence of malnutrition, especially for patients transferred from ICU. These data emphasize the importance of early nutritional screening in these patients to adapt management accordingly.

Nutrition plays a pivotal role in brain development throughout life. Sub-optimal intellectual ability and poor school performance are said to be among the long term effects of malnutrition. The aim of this study was to determine the association between nutritional status of the participants, their intelligence quotient (IQ) and academic performance.

Children aged 6-12 years who met the inclusion criteria were recruited from the public and private primary schools in the local government area using a proportionate multistage sampling technique. Weight and height were measured using standard protocols and interpreted as normal or abnormal using the World Health Organization AnthroPlus®. IQ was assessed using the Raven's Standard Progressive Matrices and was grouped into optimal and suboptimal. Academic performance was assessed using the past records of class assessment, and was classified into high, average and low academic performance. A semi-structured questionnaire was used to obtain data such as-age, gender, socioeconomic indices and family size of the study participants.

The prevalence of underweight, thinness (wasting), stunting, overweight and obesity were 2.0%, 3.6%, 2.1%, 6.7%, and 4.2%, respectively. Indices of over-nutrition were significantly associated with optimal IQ and good academic performance. There was a trend in the association between wasting and suboptimal intelligence [AOR (95%CI)=1.5 (1.0-3.0), p=0.06].

Acute and chronic under-nutrition did not adversely affect the IQ and academic performance of the study population. Gemcitabine The relationship between over-nutrition, IQ and academic performance disappeared when socio-economic status was controlled for.

Acute and chronic under-nutrition did not adversely affect the IQ and academic performance of the study population. The relationship between over-nutrition, IQ and academic performance disappeared when socio-economic status was controlled for.

Although previous research show high correlation between fat-free mass (FFM) measured by bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA), the validity of BIA to track longitudinal changes in FFM is uncertain. Thus, the aim of this study was to validate the ability of BIA to assess changes in FFM during 6 months of recovery from non-metastatic colorectal cancer (CRC).

A total of 136 women and men (50-80 years) with stage I-III CRC and a wide range of baseline FFM (35.7-73.5kg) were included in the study. Body composition was measured at study baseline within 2-9 months of surgery and again 6 months later. Whole-body BIA FFM estimates (FFM

) were calculated using three different equations (manufacturer's, Schols' and Gray's) before comparison to FFM estimates obtained by DXA (FFM

).

Correlation between changes in FFM

and FFM

was intermediate regardless of equation (r≈0.6). The difference in change of FFM

was significant compared to FFM

, using all three equations and BIA overestimated both loss and gain.