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Weight Concerns
Published in Carolyn Torkelson, Catherine Marienau, Beyond Menopause, 2023
Carolyn Torkelson, Catherine Marienau
Another important factor in making this work is to choose the right kind of carbohydrates, and plant-based carbohydrates are the way to go. You also need to eat adequate protein with every meal, a minimum of 3 oz (21 grams) at regular intervals throughout the day. Starting the day with a healthy protein breakfast will keep your metabolism running at an optimal level and prevent breakdown of muscle tissue. During the weight-loss process, you want to preserve—not lose—lean body mass while getting rid of extra body fat. Eating adequate protein may be a challenge for some of you because we seem to be surrounded by carbohydrates. So, you may need to make an effort to seek out healthy plant-based sources of protein.
Lifestyle Medicine for the Older Adult Population
Published in James M. Rippe, Manual of Lifestyle Medicine, 2021
Skeletal muscle declines with age (9). Skeletal muscle constitutes the largest soft tissue mass in the human body. It constitutes about 40% of the total lean body mass in the average, healthy young man and about 25% in a comparably aged young woman (10). Skeletal muscle and mass and strength generally peak at the age of mid-20s–30. This is then generally followed between ages 30 and 50 by a progressive decline of about 1% a year, until the age of 70 at which time the rate of loss accelerates to about 3% per year (10).
Physiology of Ageing
Published in Peter Kam, Ian Power, Michael J. Cousins, Philip J. Siddal, Principles of Physiology for the Anaesthetist, 2020
Peter Kam, Ian Power, Michael J. Cousins, Philip J. Siddal
There is a 10% decrease in lean body mass (skeletal muscle mass) with ageing, with an average loss of 6 kg of muscle mass by the age of 80 years. This is associated with an increase in the percentage of body fat and a decrease in intracellular water content. These changes are more marked in women (Figure 75.7).
Determinants of Treatment Toxicity in Patients with Soft Tissue Sarcomas
Published in Nutrition and Cancer, 2023
Katja A. Schönenberger, Emilie Reber, Karin Schläppi, Annic Baumgartner, Zeno Stanga, Attila Kollár
A recent review by Barnes et al. presented a comprehensive overview of the current literature on the impact of body mass index (BMI) and body composition on outcomes among patients with STS, highlighting the importance of obesity as a potentially targetable risk factor (2). However, interpreting BMI alone is neither simple nor meaningful as it is a poor measure of obesity. Body mass represents a combination of muscle and fat mass and does not reflect differences in lean body mass (LBM), muscle mass, and fat mass distribution (i.e., intramuscular, visceral, and subcutaneous). Decreased muscle mass is a good indicator of worse clinical outcomes and poor quality of life, especially in cases of sarcopenia, a progressive and generalized loss of muscle mass and function. Sarcopenia is prevalent in cancer and is associated with negative clinical outcomes, such as treatment toxicity, frailty, and increased morbidity and mortality (2). The depletion of muscle mass is characterized by both a reduction in muscle size (quantitative change) and an increased proportion of inter- and intramuscular fat (qualitative change). Therefore, fat infiltration may be a manifestation of the wasting process. Increased intramuscular adipose tissue can be quantified in computed tomography (CT) scans by attenuation of muscle density. Previous research has shown an association between low muscle quality (i.e., low muscle attenuation) and adverse clinical outcomes (5, 6).
Malnutrition and Weight Loss as Prognostic Factors in the Survival of Patients with Gastric Cancer
Published in Nutrition and Cancer, 2022
Zeinab Nikniaz, Mohammad Hossein Somi, Shahnaz Naghashi
We showed that the OS of well-nourished GC patients was significantly higher than the moderately or severely malnourished patients (20.5 [19.39, 21.63] months vs. 8 [5.51–10.48] months). After adjusting for confounding factors, such as cancer stage and treatment regimen, the results of the Cox-regression model indicated that the hazard of mortality rate in patients with moderate and severe malnutrition was 2.04 times higher than well-nourished patients. Most previous studies have focused on the effect of preoperative nutrition status on mortality rates, and there are very few studies evaluating the association between post-diagnosis nutritional status and cancer outcomes. Zhang et al. showed that low post-diagnosis nutrition status assessed by PNI was significantly associated with low OS (13). Some other studies have shown that malnutrition can seriously affect the immune system, leading to tumor recurrence (6). In a study in China, Tian et al. found no significant association between low BMI and survival rate in patients with esophageal cancer or GC (9). They used BMI as an indicator of malnutrition in cancer patients. However, BMI has limitations as a measure of malnutrition risk assessment; the patients may lose lean muscle mass but be classified as overweight or obese according to the BMI category. The loss of lean body mass results in morbidity and mortality problems associated with malnutrition (21).
Robust inference for skewed data in health sciences
Published in Journal of Applied Statistics, 2022
Amarnath Nandy, Ayanendranath Basu, Abhik Ghosh
We consider the data on health measurements of 706 Australian athletes from 12 different sports which were collected at the Australian Institute of Sports (AIS) in 1990 by Telford and Cunningham [53] to investigate the relationships of the five routine hematological measures, namely, the hemoglobin concentration (HC), hematocrit (H), red cell count (RCC), white cell count (WCC) and plasma ferritin concentration (PFC) in the blood of these athletes with their height (Ht), weight (Wt) and the sports type. These measurements are recorded on 1604 occasions from each athlete based on the blood samples collected from their forearm vein amidst periods of moderate to intense training but at least 6 h after a training session. Some important derived health measurements like body-mass index (BMI) and lean body mass (LBM) are also reported. The data were later used by several researchers in different statistical inference problems; in particular, few of them fitted the SN distribution with the MLE but only to a few measurements and/or a part of the data [43,61].