The rates of overweight and obesity are increasing worldwide and, in parallel, an increasing prevalence of metabolic syndrome is observed. These conditions are strongly associated with several adverse effects, including impaired quality of life and a higher risk of morbidity and mortality. Hence, there is a need for prevention strategies as well as optimal interventions to manage and reduce the incidence of these conditions as well as their co‐morbidities. According to the World Health Organization (WHO), overweight and obesity are defined as abnormal or excessive fat accumulation that causes negative health consequences (World Health Organization, 2021). The classification of overweight and obesity in adults most commonly uses the body‐mass index (BMI), which is a simple index of weight‐for‐height. It is calculated as a person’s weight in kilograms divided by the square of height in meters (kg/m2). The classification of overweight and obesity is presented in Table 18.1 (World Health Organization, 2021). The main drawback of BMI is that it does not take into account body composition and thus only provides a rough estimation of obesity and undernutrition. Table 18.1 Adult BMI and waist circumference cutoff points in white people. Source: (NICE clinical guideline 189, 2014). Overweight is defined as a BMI between 25 and 29.9 kg/m2 and obesity as a BMI >30 kg/m2 (World Health Organization, 2021). This cutoff value is not valid for non‐Caucasian ethnicities, people with increased muscle mass, such as bodybuilders, as well as children and adolescents (Durrer Schutz et al., 2019; Jensen, Ryan, Donato, et al., 2014; NICE clinical guideline 189, 2014). BMI represents the sum of fat‐mass index (FMI) and fat‐free mass index (FFMI), with the latter accounting for skeletal muscle mass, bone, and organs and FMI consisting of the peripheral and visceral adipose tissues. These components of BMI contribute differently to health status, and BMI changes are not related to a proportional and linear modification of body compartments (Donini, Pinto, Giusti, Lenzi, & Poggiogalle, 2020; Dulloo, Jacquet, Solinas, Montani, & Schutz, 2010; Rothman, 2008). Consequently, the BMI classification should be used with caution for older adults as the progressive “natural” weight gain that occurs between 20 and 65 years, could “hide” the fat‐free mass loss that happens through aging. Indeed, central/visceral fat and relative loss of fat‐free mass may become relatively more important than BMI for the assessment of obesity‐related health risks in the elderly. In association with the rapid increase of life expectancy and the rising prevalence of obesity, the so called “obesity paradox” in the elderly has been explored by many studies (more in Unit 3) (Bosello & Vanzo, 2021; Durrer Schutz et al., 2019; Flicker et al., 2010). Except for older adults, the obesity paradox also refers to evidence showing that obesity in patients with chronic diseases may be protective and associated with decreased mortality (more later in this chapter). Waist circumference is a good measure for the assessment of the visceral fat and a good predictor of cardiovascular diseases. “Normal” waist circumference references are <80 cm for women and <94 cm for men. Cutoff points indicating higher cardio‐metabolic risk are >88 cm for women and >102 cm for men (Table 18.1) (Lean, Han, & Morrison, 1995; Manolopoulos, Karpe, & Frayn, 2010; Zhu et al., 2002). Assessing body fat is not a simple task. As direct assessment of body fat is not practical, several indirect techniques and technologies have been developed over the years to assess body composition, including bioimpedance analysis (BIA), dual‐energy X‐ray absorptiometry (DXA), air‐displacement plethysmography (BodPod), and body scanning procedures (Cornier et al., 2011; Silver, E. Brian Welch, Malcolm J. Avison, & Kevin D. Niswender, 2010). The assessment of body composition (fat mass and fat‐free mass) is not a routine procedure for the management of obesity in clinical practice but may be a useful tool before and during treatment or in specific category of patients such as in older adults. However, body composition measurements are recommended for research purposes (Durrer Schutz et al., 2019; Yumuk et al., 2015). Despite growing recognition of the problem, the prevalence of overweight and obesity in the US and around the world continues to rise. In US, according to 2015–2016 data, the prevalence of overweight and obesity in men was 74.7% and 38% respectively, while the corresponding prevalence in women was 68.9% and 41.5%, respectively. It is estimated that by 2030 most American adults will suffer either from overweight or obesity, with central obesity estimated to reach 55.6% in men and 80.0% in women (Y. Wang et al., 2021). According to the WHO, the prevalence of obesity worldwide in 2016 was almost three times higher than that in 1975 (World Health Organization, 2021). Figure 18.1 shows the estimated global prevalence and numbers of adults living with obesity from 2010 to 2030. FIGURE 18.1 Estimated global prevalence and numbers of adults living with obesity in 2010–2030. Source: (WHO, 2022 / with permission of Elsevier). The causes of obesity are complex and multifactorial. The complex interaction between genetic, epigenetic, behavioral, social, and environmental factors is likely to contribute to obesity. Specifically, obesity is the result of a chronic imbalance between caloric intake and caloric expenditure. A net deficit in energy balance, either through a reduction in energy intake or an increase in energy expenditure, leads to weight reduction. Conversely, a net excess in energy balance due to a reduction in energy expenditure or an increase in energy intake, leads to weight gain (Gjermeni et al., 2021; X. Lin & Li, 2021). Peripheral signals from various body organs are integrated to control energy homeostasis. Therefore, small changes in one of these determinants can bring over time substantial changes in body weight. A host of hormones, including insulin, leptin, adiponectin, and ghrelin, communicate with the hypothalamus to control a person’s energy intake and body weight. Moreover, the heritability of obesity is estimated at between 40% and 70%, with evidence involving mutations and variations in several genes (Waalen, 2014). Obesity has been also associated with changes in the composition of the intestinal microbiota (Gjermeni et al., 2021; X. Lin & Li, 2021). Intestinal flora has been found to regulate a variety of host physiological functions, such as energy absorption, intestinal barrier permeability, secondary synthesis of bile acids, and intestinal hormone release. When metabolites of the intestinal flora enter the circulation, they can affect appetite and adipose tissue synthesis, decomposition, thermogenesis, and browning. Moreover, increased intestinal permeability is also associated with systemic chronic inflammation, which is a further factor leading to insulin resistance (K. Lin, Zhu, & Yang, 2022). Lifestyle is a major contributor to the development of overweight and obesity. A high‐energy density diet, increased portion size, lack of exercise, and a sedentary lifestyle are important risk factors for the development of obesity (X. Lin & Li, 2021). The dietary fat model of obesity postulates that high intake of fat and sugars leads to increased overall energy intake. Moreover, an increase in physical inactivity due to adopting a sedentary lifestyle, or due to the sedentary nature of many forms of work, increasing urbanization, and changing modes of transportation, all contribute to the development of obesity (World Health Organization, 2021). Obesity is a chronic systematic disease that affects almost every organ and system of the body, including the endocrine, gastrointestinal, cardiovascular, and central nervous systems (Figure 18.2) (Bischoff et al., 2017). Obesity can progressively cause and/or exacerbate a wide spectrum of co‐morbidities, including cardiovascular diseases (CVD), type 2 diabetes, hypertension, dyslipidemia, liver dysfunction, musculoskeletal disorders (especially osteoarthritis), asthma, sleep apnea syndrome, psychosocial problems, cognition, and certain types of cancer, such as colorectal, kidney, and breast cancers (Bischoff et al., 2017; Fruh, 2017; World Health Organization, 2021). The different co‐morbidities associated with obesity are shown in Figure 18.3. Consequently, the excess adiposity is a well‐established risk factor for all‐cause mortality, which is mediated through its effects on this wide range of chronic diseases. Indeed, in an early systematic review and meta‐analysis of 97 prospective, observational cohort studies, including more than 2.88 million individuals, researchers aimed to examine the effect of body weight on all‐cause mortality. They showed that those suffering from obesity (all grates) or grade II and grade III obesity, compared to normal weight, had higher risk of all‐cause mortality by 18% and 29%, respectively, while grade I obesity was not correlated with higher or lower mortality. On the contrary, overweight correlated with lower mortality risk by 6% compared to normal weight (Flegal, Kit, Orpana, & Graubard, 2013). FIGURE 18.2 Multiorgan, multi‐system complications of obesity. Source: (Bischoff et al., 2017 / with permission of Elsevier). FIGURE 18.3 Obesity and its co‐morbidities affecting many systems, organs, and tissues. Source: (Durrer Schutz et al., 2019 / with permission of Karger Publishers). Numerous studies have indicated that obesity offers a survival advantage among older adults and in patients with several chronic diseases. Indeed, although obesity is positively associated with CVD risk, it paradoxically leads to a more favorable prognosis in individuals with chronic heart failure and other cardiovascular diseases such as coronary heart disease, hypertension, and atrial fibrillation (Lavie et al., 2016; Oktay et al., 2017) (more in Chapter 12), a phenomenon commonly defined as the “obesity paradox” (S. Wang & Ren, 2018). Kokkinos et al. observed this paradox in African American and Caucasian veterans with type 2 diabetes. The cohort consisted of 4156 men with a mean age of 60 years who were followed for 7.5 years. Researchers found that individuals with a normal BMI (between 18.5 and 24.9 kg/m2) had a significantly increased risk of mortality compared to those with obesity (hazard ratio 1.70 [95% CI 1.36‐2.1]). The risk was even greater for African American, compared to Caucasian veterans. They also highlighted the important role of exercise on mortality in the same population. Those with increased exercise capacity had a lower mortality risk, independent of their weight status (Kokkinos et al., 2012). In a cohort study published two years later, in 2014, some authors of the previous study concluded to similar results; lower values of BMI (18,5‐23,9 kg/m2) compared to a BMI of 24.0 to 27.9 kg/m2 were associated with higher mortality risk in male veterans. Researchers also further examined the cardio‐respiratory fitness of the individuals and its association with BMI and mortality. The association was not present in high‐fit individuals, but only in moderate‐fit and low‐fit individuals, suggesting that the increased mortality rate in lower values of BMIs could be due to considerable weight loss accompanied by a reduction in muscle mass (Kokkinos et al., 2014). Obesity paradox is even more pronounced in older adults. A meta‐analysis of 32 population‐based cohort studies, including 197,940 participants, with an average follow up of 12 years, demonstrated that being overweight was not associated with an increased risk of mortality while the optimal BMI for people aged ≥65 years was 28 kg/m2 (Figure 18.4) (Winter, MacInnis, Wattanapenpaiboon, & Nowson, 2014). Nevertheless, according to a narrative review published in 2020, which summarized the evidence underlying the concept of obesity paradox researchers found great discrepancies between studies. The attributed causes of these discrepancies were the use of BMI in the definition of obesity, instead of excess body fat and the different adjustments for potential confounders in the studies (e.g., stage and grade of diseases, body composition etc) that could scale down the protective role of obesity in terms of mortality. However, they acknowledged a few biases among studies (e.g., reverse causation, attrition bias, selection bias of healthy obese subjects or resilient survivors) that could affect the trajectories of mortality in a number of diseases (Donini et al., 2020). FIGURE 18.4 Hazard ratio of all‐cause mortality according to BMI for men and women aged ≥65 year. Source: (Winter et al., 2014 / with permission of Oxford University Press). Metabolically healthy obesity is defined as obesity in the absence of a clearly defined cardio‐metabolic disorder, such as MetS, insulin resistance, hypertension, diabetes, or dyslipidemia. Identifying individuals with metabolically healthy obesity may potentially help avoid wasting time, effort, and resources on people who may not benefit—or benefit less—from weight‐loss management. However, the term “metabolically” is often misinterpreted as meaning without any sort of complications whatsoever and, consequently, without the need for treatment. Besides cardio‐metabolic diseases, obesity may be associated with orthopedic problems, reproductive disorders, depression, asthma, sleep apnea, renal disease, back pain, skin infections, cognitive decline, social stigma, and overall reduced quality of life (Magkos, 2019) and thus individuals with metabolically healthy obesity will seek assistance from a healthcare professional at some point in their life. However, meta‐analyses of prospective studies have demonstrated that metabolically healthy individuals with obesity have approximately half the risk of developing type 2 diabetes and CVD compared with metabolically unhealthy individuals with obesity (Bell, Kivimaki, & Hamer, 2014; N. Eckel, Meidtner, Kalle‐Uhlmann, Stefan, & Schulze, 2016). Nevertheless, the cardio‐metabolic disease risk is still significantly elevated by 50% to 300% compared with metabolically healthy lean individuals. Therefore, there is an obvious need to deal with both phenotypes of obesity, both unhealthy and healthy (Magkos, 2019). Factors that contribute to obesity in life are already present from the time of gestation. The first months of life seem to play a crucial role in the development of obesity later in life. Preventing overweight or obesity from the early stages of life could be an important strategy (M. et al., 2013). A position paper from the Academy of Nutrition and Dietetics published in 2013 proposed interventions for the prevention of excess body weight in children and treatment of children with overweight or obesity (Table 18.2) (Hoelscher, Kirk, Ritchie, & Cunningham‐Sabo, 2013). Breastfeeding has been associated with a modest reduction in the risk of developing overweight or obesity later in life by inducing lower plasma insulin levels, thereby decreasing fat storage and preventing excessive early adipocyte development. The higher protein content of artificial infant milk compared to the lower‐protein content in breast milk is responsible for the increased growth rate and adiposity, promoting growth acceleration, whereas breastfeeding has been shown to promote slower growth. Therefore, enhancing breastfeeding can be suggested to reduce the risk of obesity (Wendy H Oddy, 2012). Table 18.2 Summary of recommendations from the review of child obesity primary prevention literature. Source: (Hoelscher et al., 2013). FIGURE 18.5 Primary, secondary, tertiary, and quaternary preventions with their respective explanations. Source: (Durrer Schutz et al., 2019 / with permissions of Karger Publishers). Although the development of overweight and obesity is multifactorial, the decline in energy expenditure is considered as one of the most important determinants of excessive body weight. Body weight increases because of a positive energy balance when energy intake exceeds energy expenditure. So, aiming to or maintaining a healthy weight, which is achieved when energy intake is adjusted to energy expenditure is considered the main goal for preventing overweight/obesity (Lavie et al., 2018). Four categories of prevention—primary, secondary, tertiary, and fourth (quaternary prevention)—are shown in Figure 18.5 (Durrer Schutz et al., 2019). Counseling people to reduce the intake of sugar and the consumption of sugar‐sweetened beverages (SSBs) is important for controlling body weight and therefore the risk for obesity‐related chronic diseases (Ebbeling, 2014). A meta‐analysis of 30 randomized controlled trials (RCTs) and 38 prospective cohort studies showed that intake of sugar or SSBs is a determinant of body weight among free‐living people involving ad libitum diets (Morenga, Mallard, & Mann, 2013). Moreover, high intake of SSBs has been linked to higher risk of type 2 diabetes, CVD, and some types of cancers. SSBs are believed to promote weight gain through incomplete compensation for liquid calories at subsequent meals (Malik & Hu, 2019). A low‐calorie healthy food pattern either based on the components of a vegetarian or a Mediterranean dietary pattern may offer a possible solution to the ongoing challenges to prevent and manage obesity and CVDs. A meta‐analysis of 12 cross‐sectional studies and one case‐control study, including 26,974 and 174 participants, respectively, demonstrated that a posteriori healthy dietary patterns characterized as high in fruits, vegetables, and whole grains decrease the risk of central obesity (Rezagholizadeh, Djafarian, Khosravi, & Shab‐Bidar, 2017). Moreover, in a meta‐analysis of six prospective cohort studies, including a total of 244,678 participants, higher adherence to the Mediterranean diet (MD) was associated with a 9% lower risk of suffering from overweight and/or obesity, while closer adherence to the MD, as assessed by the MD score was inversely associated with weight gain over 5 years (Lotfi, Saneei, Hajhashemy, & Esmaillzadeh, 2022). The role of physical activity in the prevention of excessive body weight is well‐documented. As mentioned in previous chapters, the World Health Organization in the 2020 guidelines provides age‐appropriate recommendations for PA for children, adults and older adults (WHO, 2020). In brief, adults (from 18 to 64 years old) should undertake 150–300 min of moderate‐intensity physical activity, or 75–150 min of vigorous‐intensity physical activity, or some equivalent combination of moderate‐intensity and vigorous‐intensity aerobic physical activity, per week. Among children and adolescents, an average of 60 min/day of moderate‐to‐vigorous intensity aerobic physical activity across the week provides health benefits. Older adults should also participate in varied multi‐component physical activity emphasizing functional balance and strength training (i.e., circuit training) at moderate or greater intensity, 3 or more days a week. Functional exercises include tandem and one‐leg stands, squatting, chair stands, toe raises, and stepping over obstacles (WHO, 2020). According to 2019 European guidelines for adults with obesity (Durrer Schutz et al., 2019), the main goals of obesity treatment are to prevent complications, to prevent or treat co‐morbidities if they are present, to reduce stigmatization, and to induce well‐being, positive body image, and self‐esteem. Body weight per se is not a priority, while obesity treatment cannot focus only on weight reduction. The goals can focus more on lifestyle changes, reducing waist circumference, and body composition than weight loss. Modest weight loss (i.e., 5–10% of the initial body weight) within 6 months may lead to significant clinical benefits, and 5–15% of weight loss can be feasible depending on the pathology (Durrer Schutz et al., 2019; Yumuk et al., 2015). Individuals suffering from overweight or obesity, in most cases, require weight loss. However, there are patients that are not eligible or prepared for this kind of intervention. The main goal is to assess every individual and provide tailor‐made advice and customized intervention based on a person’s needs. According to the 2013 American Heart Association/American College of Cardiology task force on practice guidelines and The Obesity Society (AHA/ACC/TOS) guideline for the management of overweight and obesity in adults, a weight‐loss intervention should be performed in individuals with obesity or overweight, who additionally have one indicator of increased cardiovascular risk or any obesity‐related comorbidity. Risk factors indicating increased cardiovascular risk are diabetes or prediabetes, hypertension, dyslipidaemia, and elevated waist circumference. It is possible, though, that some of these patients who need weight loss might reject the intervention. In this case, a patient’s intention in losing weight should be frequently assessed and they should be advised to avoid further weight gain. Individuals with a normal body weight or individuals with overweight without cardiovascular disease risk factors or co‐morbidities linked to obesity should not be advised to lose weight, but to avoid additional weight gain. Furthermore, generally healthy individuals with overweight or currently normal weight individuals who used to suffer from overweight or obesity should be advised to measure their body weight, alter their energy intake, when appropriate, and engage with physical activity to avoid weight gain (Jensen, Ryan, Donato, et al., 2014). In older adults with overweight, weight‐reducing diets may be avoided so as to prevent loss of muscle mass and functional decline, whereas in older adults with obesity, weight‐reducing diets may only be considered after weighing the benefits and risks (Volkert et al., 2019). Over the past decades, a considerable number of diets have been used to induce weight loss in people living with overweight or obesity (examples in Table 18.3). Even though there are a lot of differences between them, all of them aim at the same thing, caloric restriction. What varies is the way of achieving this. The question is, which is the most effective? A low‐calorie diet (LCD) usually ranges from 1200 to 1600 kcal/d with the use of a meal plan, in which all food choices and portion sizes for all meals and snacks are provided. The use of meal replacements, usually liquid shakes and bars, containing a known amount of energy and macronutrient content, is also appropriate. These methods enhance adherence to a LCD by reducing problematic food choices and decreasing any challenges in making healthy choices (Raynor & Champagne, 2016). A meta‐analysis of six RCTs showed that an LCD composed of conventional foods or meal replacements led to a 2.54 kg and 2.43 kg greater weight loss in the partial meal‐replacement group for the 3‐month and 1‐year follow ups, respectively (Heymsfield, Van Mierlo, Van Der Knaap, Heo, & Frier, 2003). A very low‐calorie diet (VLCD) provides ≤800 kcal per day and are usually consumed as liquid shakes. Specifically, a VLCD is usually appropriate only for individuals with a BMI ≥30 kg/m2 and is used before bariatric surgery to reduce overall surgical risk in individuals with severe obesity. A meta‐analysis of six RCTs showed that VLCDs produce significantly greater weight loss in the short‐term compared to LCD, whereas there was no difference in weight loss between the diets in long‐term follow up (>1 year) (Tsai & Wadden, 2006). Although calorie intake seems to play a crucial role for the management of overweight or obesity, current evidence indicates that different diets with different macronutrient content (low‐carbohydrate diets‐ low CHO, carbohydrate intake ≤ 40% vs. low‐fat diets‐LFD, fat intake < 30%), may promote similar percentages of weight loss while it is the adherence to each diet that will predict their success. According to a systematic review and meta‐analysis comparing low CHO diets with isoenergetic balanced weight‐loss diets in adults with overweight or obesity, there is little or no difference in weight loss or cardiovascular risk factors when comparing low CHO diets with balanced diets as long as they are isoenergetic. Results are similar either assesing the individuals in the short (3–6 months) or in the long term (up to two years of follow‐up). (Naude et al., 2014). Another research team resulted in a similar finding. When they compared a LFD with low CHO diets or higher‐fat diets, they inferred that it is the intensity of the intervention that most strongly affect the final weight loss and not the macronutrient intake (Mansoor, Vinknes, Veierod, & Retterstol, 2016). Table 18.3 Common weight‐loss diets. Source: (Jensen, Ryan, Apovian, et al., 2014) Nevertheless, in a meta‐analysis of 11 RCTs of, at least, a 6‐month duration, individuals with obesity followed a low CHO diet and compared to those who followed an LFD. The low CHO diet showed a greater decrease in body weight (mean difference ~2.2 kg) and in triglycerides as well as a greater increase in high‐density lipoprotein‐cholesterol (HDL‐cholesterol) and in low‐density lipoprotein‐cholesterol (LDL‐cholesterol) compared to the LFD (Mansoor et al., 2016). This last consequence should be taken into consideration based on patient’s medical history and risk factors to assess the pros and cons of losing weight but also increasing LDL. Another meta‐analysis of 13 RCTs, with durations of 12 months or more, compared a very low CHO‐ketogenic diet with a typical LFD with energy restriction. The low CHO diet resulted in a greater weight loss (mean difference 0.9 kg), (Mansoor et al., 2016), while an amelioration in diastolic blood pressure was also observed (Bueno, De Melo, De Oliveira, & Da Rocha Ataide, 2013). These results indicate that health care providers may choose among different dietary options to manage individuals with overweight or obesity and that there is not an optimally effective diet for all individuals to lose weight. Moreover, a higher level of adherence to a dietary weight‐loss approach, regardless of the type of the diet will determine its success (Freire, 2020). Higher adherence to a diet is significantly correlated with the amount of weight loss and lower weight regain compared to less or no weight loss observed in individuals with difficulties in adhering to a weight‐loss dietary program (Freire, 2020). High‐protein diets (at least 20% of energy derived from protein) for weight loss have also been thoroughly studied, mainly as a means to maintain lean mass. A meta‐analysis of 37 RCTs demonstrated that people with increased protein intake (ranging from 18–59% of energy requirements) reduced body weight by 1.6 kg compared to controls (digestible carbohydrate, fiber, fat, or no supplementation [no placebo used]). The same study concluded that people with prediabetes may benefit more from a diet high in protein compared to people with normoglycemia (Hansen, Astrup, & Sjödin, 2021). Another meta‐analysis of 20 RCTs, including 1174 participants, showed that older adults aged ≥50 years retained more lean mass while losing body fat mass during weight loss when they consumed energy‐restricted higher protein (≥25 energy percentage) rather than normal protein diets (<25 energy percentage) (Kim, O’Connor, Sands, Slebodnik, & Campbell, 2016). Regarding CVD factors, meta‐analyses have concluded that a high‐protein diet is not superior to a standard diet during weight loss on changes in serum triglycerides, HDL, and LDL‐cholesterol compared to a standard diet during weight loss (Santesso et al., 2012; Wycherley, Moran, Clifton, Noakes, & Brinkworth, 2012). Finally, diets high in protein have not been found to beneficially affect hunger and the desire to eat, food cravings, or overall well‐being (Li, Armstrong, & Campbell, 2016; Nerylee A. Watson et al., 2018; Nerylee Ann Watson et al., 2018). Whatever the intervention is for the management of overweight or obesity, it is important to refer each individual who is willing to lose weight to a qualified registered dietitian that can evaluate which type of diet will be more effective for the patient. The aim is to achieve the greatest adherence to the weight‐loss diet, so that the patient can benefit as much as possible. The summary findings of these studies on dietary modifications for weight loss in individuals with overweight or obesity are shown in Table 18.4 (Yannakoulia, Poulimeneas, Mamalaki, & Anastasiou, 2019). However, there is a great number of individuals who choose to follow, without any supervision, the so called “fad diets” for weight loss. Fad diets are popular in promising easy and quick weight loss. Typical examples of such diets are shown in Table 18.5 (Mattson, Longo, & Harvie, 2017). These diets may have a positive impact on health as they are based on extreme caloric restriction, leading to rapid weight loss. What is concerning is the safety of these methods and the tendency for individuals to regain weight once they start consuming a normal diet again (Obert, Pearlman, Obert, & Chapin, 2017). As nutrition research and epidemiology have focused on the relationship between health/disease and dietary patterns—rather than single nutrients, the role of specific dietary patterns in the management of overweight and obesity has been evaluated. The MD dietary pattern is the most extensively studied pattern for a variety of health outcomes, including obesity and weight treatment. The PREDIMED study is a large study, with two MD intervention arms, one supplemented with olive oil and the other supplemented with nuts, and a low‐fat diet as the control group. All diets were provided without an energy restriction. The results of this trial did not reveal a difference between the three arms regarding body weight over 5 years. In contrast, waist circumference increased less in the MD plus nuts group compared with the control group (Estruch et al., 2019). Moreover, another meta‐analysis of nine RCTs, including 1178 diabetic individuals, demonstrated that a MD improved outcomes of glycemic control, weight loss, and CVD risk factors (Huo et al., 2015). In an RCT including participants with central obesity, the Nordic diet (high in fruits, vegetables, whole grains, and fish) provided ad libitum, resulted in ~3.5 kg larger weight loss compared to a group receiving an average Danish diet (Poulsen et al., 2014). Moreover, a meta‐analysis of 12 RCTs, including 1151 participants, concluded that vegetarian diets had significant benefits on weight reduction compared to non‐vegetarian diets (Huang, Huang, Hu, & Chavarro, 2016). Table 18.4 Summary findings of the studies on dietary modifications for weight loss in people with overweight and obesity, published beyond the 2013 AHA/ACC/TOC guidelines. Source: (Yannakoulia et al., 2019 / with permission of Elsevier). Table 18.5 Common fad diets. Source: (Adapted from Mattson et al., 2017). Table 18.6 General nutritional and behavioral advice given to individuals with obesity. Source: (Durrer Schutz et al., 2019).
CHAPTER 18
Obesity and Metabolic Syndrome
INTRODUCTION
DEFINITION, ASSESSMENT, AND CLASSIFICATION OF OBESITY
Obesityclass
BMI (kg/m2)
Waist circumference
Associated health risk
Underweight
<18.5
Normal
18.5–24.9
Average
Overweight
25.0–29.9
Men: ≥94 cm
Women: ≥80 cm
Increased
Obesity
I
30.0–34.9
Men: ≥102 cm
Women: ≥88 cm
Moderate
Moderate obesity
II
35.0–39.9
High
Severe obesity
III
≥40
Very high
EPIDEMIOLOGY OF OBESITY
PATHOGENESIS OF OBESITY
COMPLICATIONS AND CO‐MORBIDITIES OF OBESITY
OBESITY PARADOX
METABOLICALLY HEALTHY OBESITY
PREVENTION OF OBESITY
EARLY STRATEGIES
HEALTHY WEIGHT
DIETARY SUGAR
DIETARY PATTERNS
PHYSICAL ACTIVITY
TREATMENT OF OBESITY
GOALS
WHO NEEDS WEIGHT LOSS?
DIETARY STRATEGIES
Diet name
Composition
Need for prescribed energy restriction
Low‐calorie
Not specific
Prescribed energy restriction.
Higher protein
Choice of foods that lead to an energy deficit.
Higher protein Zone™‐type
5 meals/d, each with:
Without prescribed energy restriction. Automatically leads to energy deficit.
Lacto–ovo–vegetarian–style
Typical diet composition
Prescribed energy restriction.
European Association for the Study of Diabetes Guidelines
Focuses on targeting food groups and achieving an energy deficit.
Without prescribed energy restriction.
Low‐carbohydrate
<20 g/d carbohydrate
Without prescribed energy restriction. Automatically leads to energy deficit.
Low‐fat vegan‐style
Without prescribed energy restriction. Automatically leads to energy deficit.
Low‐fat
20% energy from fat
Without prescribed energy restriction. Automatically leads to energy deficit.
Low–glycemic load
Food choices with low–glycemic load
With or without prescribed energy restriction but leading to energy deficit either way.
Lower‐fat, high‐dairy diets with or without increased fiber and/or low–glycemic index (low–glycemic load)
Prescribed energy restriction.
Macronutrient‐targeted
Prescribed energy restriction.
Mediterranean‐style
Typical MD food choices
Prescribed energy restriction.
Moderate‐protein
Choice of foods that lead to an energy deficit.
High‐glycemic or low–glycemic load
Based on food glycemic load
Prescribed energy restriction.
The AHA‐style Step 1 diet
Prescribed energy restriction of 1500 to 1800 kcal/d.
Dietary intervention
Summary of findings
Very low‐carbohydrate diets
Low‐carbohydrate diets
High‐protein diets
Intermittent fasting/severe energy restriction
Meal replacements
Diets promoting specific food groups
Diets close to the MD
Diets with varying energy distribution throughout the day
Diet Name
Composition
Juicing or detoxification
All meals are replaced with juices
Sometimes they contain supplements
Duration: 2–21 days
Paleo (Paleolithic)
Includes only food that existed in the Stone Age
Foods included: fresh vegetables, fruit, lean meats, poultry, fish, eggs, tofu, nuts, seeds
Foods prohibited: cereals, grains, legumes, and dairy
Intermittent fasting
Fasting for an extended period (16–48 h) with little or no calorie intake, followed by periods of normal eating
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