8
Risk Characterization and Public Health Implications
The Dietary Reference Intake (DRI) organizing framework includes a discussion of the public health implications of relationships between energy intakes that deviate from Estimated Energy Requirement (EER) values and adverse health outcomes, primarily a characterization of the risk of chronic diseases (see Chapter 2 for a description of the organizing framework). This framework also includes information on special vulnerable populations for whom the EER values may need to be adjusted.
In this chapter, evidence from published systematic reviews, augmented when appropriate with other relevant scientific publications, are summarized. Because of inaccuracies in reported energy intakes, this approach relies on indicators of energy intake imbalances as exposures for evaluating the risks of adverse health outcomes for studied age and sex groups, primarily from high-income countries. The following discussion summarizes evidence that the committee deemed most relevant to its task. The approach for selecting and evaluating published systematic reviews is described in Chapter 3, and additional detail is contained in Appendixes D, E, and F.
CHRONIC DISEASE RISKS ASSOCIATED WITH OVERWEIGHT AND OBESITY
Population data from Tables 6-5 and 6-6 in Chapter 6 show a high prevalence of overweight and obesity in U.S. and Canadian populations. To maintain body weight over time, energy intake must equal energy
expenditure. If energy intake exceeds energy expenditure, weight gain will result. Questionnaire data from the U.S. National Health and Nutrition Examination Survey (NHANES) indicate a significant prevalence of attempts by study participants to lose weight currently or in the past year: 10 percent for normal weight males 19 years or older and 27 percent for adult women. Among overweight adults, the percentage is 32 percent for adult males and 47 percent for adult females. Higher percentages are observed for obese adults: 44 percent for males and 50 percent for females (see Appendix Tables L-15, L-16, and L-17). Thus, not only are the prevalence of overweight and obesity high among the U.S. and Canadian populations, but the NHANES data also indicate a relatively high prevalence of trying to lose weight. To evaluate the associations between weight gain or obesity and the risk of chronic diseases, the committee relied on measures of body weight and adiposity as indicators of exposures rather than on self-reported dietary intakes because of significant inaccuracies in the reporting of actual energy intakes.
People who have overweight or obesity are more prevalent in the United States and Canada compared to those who are underweight; however, self-reported dietary intakes are an inaccurate indicator of actual energy intakes, and body weight is reflective of past energy imbalance states. Nevertheless, body weight is frequently used as the exposure that best indicates a state of energy imbalance.
The committee carried out an umbrella review (described in Chapter 3) of published systematic reviews to evaluate associations of body mass index (BMI), weight change, and weight cycling with the risk of several chronic disease outcomes and all-cause mortality. Three exposure measures were used as indicators of deviations from meeting energy requirements to achieve energy balance: the effect of BMI, the effect of weight change, and the effect of weight cycling. The umbrella review served as the committee’s primary data source. For topics for which no existing systematic reviews were identified and the committee considered to be high priority, the umbrella review was supplemented with relevant longitudinal or population-based studies from peer-reviewed published literature. Outcomes included hypertension and cardiovascular disease and mortality, some cancers, all-cause mortality, and diabetes. Appendix J presents tabulated summaries of the number of studies within the systematic review, participant characteristics, study designs, methods used, exposures, and outcomes extracted from the literature discussed below.
Body Mass Index and Body Composition
In both clinical and community settings, calculation of body mass index (BMI; defined as weight in kilograms divided by the square of height in
meters) remains the easiest and most readily accessible tool for identifying individuals at risk of adverse health outcomes related to being overweight or underweight (Gonzalez et al., 2017). BMI is an insensitive measure, however, because it assumes that an optimal weight range exists, regardless of the proportion of fat to fat-free mass. Many more sophisticated methods, compared to the calculation of BMI, are available to measure fat and fat-free mass precisely and reliably, including bioelectric impedance, ultrasound, and imaging modalities such as dual energy x-ray absorptiometry, computed tomography, and magnetic resonance. However, these techniques are primarily used in research settings. The most frequently stated limitation of BMI is that individuals with a high lean body mass (i.e., skeletal muscle) might have a high BMI without having excess body fat. Moreover, BMI does not account for interindividual variability by age, sex, ethnicity, or health status (Pasco et al., 2012).
Anthropometric techniques, such as waist circumference, waist–hip ratio, and waist–height ratio offer the potential to better discriminate cardiometabolic risk than BMI (Darbandi et al., 2020). Nonetheless, BMI has continued to be used as a proxy for adiposity and chronic disease risk since the original publication of BMI classifications by the World Health Organization (WHO).
BMI Categories
The National Institutes of Health (NIH) and the Centers for Disease Control and Prevention (CDC) guidelines use the original cutoffs identified by WHO to define adult individuals as having underweight (BMI < 18.5), normal weight (BMI 18.5–24.9), overweight (BMI 25.0–29.9), or obesity (≥ 30). A 2006 update of the WHO classifications included categories to identify severe underweight (< 16.5) and to further define obesity risk as class I (BMI 30.0–34.9), class II (BMI 35.0–39.9) and class III (≥ 40.0) in adults (WHO, 2016a).
Strong evidence has demonstrated that the WHO cutoffs underestimate obesity-related health risk in Asian adults. For example, the China Health and Nutrition Survey identified a lower threshold for overweight in the BMI range of 22.5 to 25.9 for males and 22.8 to 26.6 for females (He et al., 2015). Consequently, more recent cutoffs have been adjusted to a BMI of 23–24.9 for overweight and BMI ≥ 25 for obesity in Asian populations (Weir and Jan, 2022).
Additional adjustment in BMI classification may be needed for older adults. The Canadian Longitudinal Study on Aging identified age-specific BMI thresholds for older adults that are associated with cardiometabolic health outcomes. This study also compared the performance of these thresholds against the WHO BMI cutoffs for comparable age groups.
Findings indicated that for adults 65 years and older, the BMI threshold should be higher than the WHO cutoffs, with overweight defined at a BMI of 26.9 in adults aged 65 to 74 years and 26.6 in adults ≥ 75 years (Javed et al., 2022).
BMI cutoffs for children and adolescents are identified by percentiles or Z-scores based on sex and age group. Some evidence indicates a need for a better approach to defining BMI cutoffs in children and adolescents based on ethnicity. For example, a study in Indian (South Asian descent) and Creole (African/Madagascar descent) children aged 7 to 13 years conducted in Mauritius showed that when matched by BMI, Indian children had a higher percentage of body fat than Creole children. This finding suggests that the WHO BMI cutoffs for overweight and obesity would need to be lowered by 4.6 to 5.9 units in Indian and 2.0 to 3.7 units in Creole children (Ramuth et al., 2020).
BMI and Chronic Disease Risk
Prevalence of high BMI has reached epidemic proportions worldwide. Numerous studies have shown a strong linear relationship between high BMI and an increased risk for chronic disease. Although risk for some chronic disease states, such as cardiovascular diseases, may be better predicted by waist circumference, waist–hip ratio, or waist–height ratio, the body of evidence based on systematic reviews, meta-analysis, and Mendelian randomization indicate a profound relationship between high BMI and functional disabilities, impaired quality of life, serious disease states, and mortality.
Diabetes
Evidence from systematic reviews
A systematic review by Zhang et al. (2021) found that prepregnancy BMI ≥ 25.0 increases risk 2.64-fold for having gestational diabetes. Further, during pregnancy, every additional 5 units in BMI was associated with a 10 percent increased risk for type 1 diabetes (Hidayat et al., 2019). This relationship between high BMI during pregnancy and type 1 diabetes was nonlinear, such that a steeper increase in risk occurs with BMI ≥ 26.0. In children, a high BMI was shown to increase the risk for childhood and adolescent asthma, prediabetes, hypertension, and nonalcoholic fatty liver disease (NAFLD) (Azizpour et al., 2018; Sharma et al., 2019). Children and adolescents with a BMI equal to or greater than the 85th percentile compared to those who have normal or underweight have a 64 to 92 percent increased risk for asthma, a 40 percent increased risk for prediabetes, a 4.4-fold increased risk for hypertension, and a 26-fold increased risk for NAFLD.
Among young and middle-aged adults, having a BMI ≥ 25.0 is associated with the risk of numerous chronic disease conditions. Yu et al. (2022) conducted a systematic review for associations between underweight and type 2 diabetes and between weight status and prediabetes. The review included prospective cohort studies with a minimum 12-month follow-up period. The primary analyses of diabetes risk were performed using the Asian versus non-Asian BMI classifications with additional analyses for risk of prediabetes or type 2 diabetes. The analyses found that overweight and obesity were associated with a 24 percent increased risk for prediabetes, while overweight was associated with a 2-fold increased risk and obesity a 4.5-fold increased risk for type 2 diabetes.
A systematic review to determine whether associations exist between sarcopenic obesity and risk of type 2 diabetes in adults with overweight and obesity found a 38 percent increased risk for type 2 diabetes among those with sarcopenic (compared to nonsarcopenic) obesity (Khadra et al., 2019).
Larsson and Burgess (2021) reviewed evidence for a causal association between BMI and chronic diseases. A meta-analysis of mendelian randomization (genetically predicted BMI in relation to chronic disease) studies showed a high adult BMI as a causal risk factor for a number of chronic diseases, in particular type 2 diabetes, which showed a 2-fold increased risk with a BMI ≥ 25.
Evidence from peer-reviewed literature
In a longitudinal analysis of 1,168,418 women using Behavioral Risk Factor Surveillance System (BRFSS) 2006–2010 survey data, Ibe and Smith (2014) assessed population-level changes in the prevalence of diabetes among women with no known risk factors and the influence of those changes on diabetes-related outcomes. In the study population of 18- to 64-year-old women, after adjusting for age, race, physical activity, and year of survey response, the analysis indicated a 3.5-fold increase in diabetes in those with a BMI > 25. There was also an approximately 30 percent projected increase in odds of diabetes diagnoses for this population in the subsequent 10 years.
Hypertension and Cardiovascular Disease (CVD)
Evidence from systematic reviews
A systematic review by Jayedi et al. (2018) analyzed 57 prospective cohort studies for associations between anthropometric measures and risk of developing hypertension. Studies that reported risk estimates of hypertension for three or more quantitative categories of indices of general and abdominal adiposity were included in the review. Overall, the review found that each 5-unit increase in BMI above 20.0 was associated with a 49 percent increased risk of hypertension.
Zhou et al. (2018) conducted an intake–response meta-analysis of 57 cohort studies examining the relationship between multiple adiposity measures and incidence of hypertension. The study included 125,071 incident cases among 830,685 participants. Results of the meta-analysis found at least a 50 percent increase in the risk for hypertension for every 5-unit increase in BMI, suggesting that in the normal range of BMI values, leanness may contribute to preventing hypertension incidence.
Liu et al. (2018b) carried out a systematic review and meta-analysis of prospective studies to understand the strength and shape of the intake–response relationship between BMI and the risk of stroke. This review found that the risk of stroke increases by 10 percent for every 5-unit increase in BMI for those with a BMI > 23 to 24, but not for those with lower BMIs, and the risk was greater for males than for females
In a systematic review and meta-analysis, Dugani et al. (2021) evaluated the magnitude of associations between various risk factors and premature myocardial infarction (MI) in males and females aged 18 to 65 years. Among the findings were that males with overweight or obesity have an almost 2-fold increased risk for premature myocardial infarction. Other systematic reviews for the risk of CVD found that waist circumference or waist–height ratio were better predictors of risk than BMI. Darbandi et al. (2020) found that BMI, waist circumference, and waist–height ratio have moderate power to identify a risk for CVD and that in adults, waist circumference and waist–height ratio were better predictors of CVD than BMI. In a review of mendelian randomization studies, Kim et al. (2021) showed high BMI as a causal risk factor for CVD outcomes. Specifically, each 5-unit increase in BMI increased risk for CVD events.
Evidence from peer-reviewed published literature
In a prospective cohort study, Rexrode et al. (2001) compared waist circumference and waist–height ratio as predictors of coronary heart disease (CHD) and to determine if there was an association with disease independent of BMI in over 20,000 men participating in the Physicians’ Health Study. The study found that among men with a BMI ≥ 27.6, there was a 73 percent increased risk for a CHD event, suggesting an association between abdominal adiposity and elevated risk of CHD in middle-aged and older men.
A cohort of 5,209 Framingham Heart Study participants were examined for a relationship between BMI and morbidity and mortality from CHD (Kim et al., 2000). In this 24-year follow-up study, the relative risk for CHD among male participants was found to be 28 percent for BMI ≥ 23.8, 45 percent for BMI ≥ 25.9, and 53 percent for BMI ≥ 28.2. Among female participants, risk of CHD-related death was 86 percent higher for BMI > 27.61 compared to BMI < 22.34.
A community longitudinal study to assess risk for diabetes or CVD stratified by BMI and the presence or absence of metabolic syndrome or insulin resistance found that 2,902 females and males with BMI ≥ 25.0 had a 3-fold increased risk for CVD. Taken together, risk factor clustering or insulin resistance appeared to confer much of the risk for diabetes or CVD commonly associated with high BMI (Meigs et al., 2006). Another community-based longitudinal study of 2,316 males with type 2 diabetes and BMI ≥ 25.0 assessed their risk of CVD and mortality and found that males with overweight and obesity with diabetes have a similar 2.7-fold increased risk (Church et al., 2005).
Cancers
Evidence from systematic reviews
Sohn et al. (2021) examined the risk of hepatocellular cancer in a systematic review of studies of men and women 18 years and older with a BMI ≥ 25.0. This review found that the risk for liver cancer increased in a BMI-dependent manner, with a 36 percent increased risk for BMI > 25, 77 percent increased risk for BMI > 30, and 3-fold increased risk for BMI > 35 (and a 70 percent increased risk of hepatocellular cancer overall for BMI ≥ 25).
Premenopausal and postmenopausal breast, endometrial, and ovarian cancer risk among women was assessed based on their early-life (age ≤ 25 years) BMI in a systematic review by Byun et al. (2022). Across 37 studies that included 1.8 million women each 5-unit increase in early-life BMI was associated with a 16 percent reduced breast cancer risk in premenopausal and postmenopausal women. Across 10 studies that included 662,779, each 5-unit increase in early-life BMI was associated with a 1.4-fold increased endometrial cancer risk. Across six studies that included 496,391 participants, each 5-unit in BMI increase in early life was associated with a 15 percent increased ovarian cancer risk.
A systematic review of 28 prospective cohort studies, with 28,784,269 participants and 127,161 lung cancer cases, examined associations between BMI and lung cancer risk. The review found that higher BMI was associated with lower lung cancer risk overall, but that multiple confounders, including smoking, preclinical cancer, and time lag affected the association. Furthermore, in contrast, highest category waist circumference (versus lowest category) was associated with 26 percent increased lung cancer risk (Gao et al., 2019).
Gu et al. (2022) searched previously published systematic reviews and meta-analyses of cohort studies to identify potential risk factors for prostate cancer. A two-sample mendelian randomization analysis was used to validate potentially causal relationships. This study found that higher
BMI was associated with a 1 percent decreased risk for localized prostate cancer, consistent with previous mendelian randomization studies.
A meta-analysis by Hidayat et al. (2018) identified associations between anthropometric factors and non-Hodgkin’s lymphoma. Among more than 7 million males and females aged 18 years and older, each 5-unit increase in BMI was associated with a 6 percent increased risk for non-Hodgkin’s lymphoma, with no difference by sex. Further, each 5-unit increase in BMI in early adulthood (18–21 years) was associated with an 11 percent increased risk for non-Hodgkin’s lymphoma.
Liu et al. (2018a) conducted a meta-analysis of 24 cohort studies with almost 9 million participants to examine associations between BMI and kidney cancer risk in males and females aged 18 years and older and with a BMI > 20. An increased kidney cancer risk of 1.06 (95% CI, 1.05–1.06) for each 1-unit increase in BMI > 20 was found in this intake–response meta-analysis.
A systematic review of an overlapping set of epidemiological studies on the association of BMI with early-onset colorectal cancer risk was conducted in males and females aged less than 55 years old with BMI ≥ 25.0 (Li et al., 2021). Both overweight and obesity were associated with a 42 percent increased risk of early-onset colorectal cancer. O’Sullivan et al. (2021) conducted a systematic literature review of studies examining nongenetic risk factors for early-onset colorectal cancer in adults aged less than 50 years old. Obesity (BMI ≥ 30) was associated with a 54 percent increased risk of early onset colorectal cancer, with males at higher risk than females.
Li et al. (2016) examined BMI and gallbladder cancer risk in a systematic review of more than 9 million individuals aged 18 years and older with a BMI of 25.0 or greater. The pooled risk for gallbladder cancer for overweight was 10 percent and obesity 58 percent, and the risk of gallbladder cancer increased by 4 percent for each 1-unit increase in BMI.
Youssef et al. (2021) conducted a systematic review to evaluate the effect of BMI and weight change over time on the risk of developing thyroid cancer. The study included more than 24 million individuals aged 18 years and older with BMI < 18.5 or ≥ 25.0. The analysis found a 26 and 50 percent, respectively, increased risk of thyroid cancer associated with overweight and obesity, with the risk greater in females than in males. Having an underweight BMI decreased risk by 32 percent.
Disability
Evidence from systematic reviews
Jiang et al. (2019) conducted a systematic review and intake–response meta-analysis of 37 studies on all-cause mortality and 9 on disability to examine associations between BMI
and disability in adults aged 65 years and older. The study found that a BMI of 24.0 to 28.0 decreased risk for disability by 4 percent, but BMI > 28 increased disability risk by 19 percent. Mortensen et al. (2021) carried out a systematic review to assess various modifiable risk factors for hip fracture. BMI < 18.5 was associated with almost a 3-fold increased risk for fragility hip fracture, whereas a BMI > 30 decreased hip fracture risk by, on average, 42 percent.
All-Cause Mortality
Evidence from systematic reviews
In addition to assessing the risk of hip fracture, Jiang et al. (2019) examined all-cause mortality. Adults aged 65 years and older with BMI < 23.0 and > 33.0 had increased risk for all-cause mortality. Kitahara et al. (2014) estimated sex- and age-adjusted total and cause-specific mortality rates and multivariable-adjusted hazard ratios across 20 prospective studies for adults aged 19 to 83 years at baseline. The study found that, compared with lower BMI (18.5–24.9), adults with a BMI of 40 or higher had incrementally increased risks for death (adjusted hazard ratios ranging from 2.25 to 5.91). The increased risks of death with higher BMI were somewhat greater for males than for females.
Weight Change and Chronic Disease Risk
Studies designed to evaluate the relationships of body weight and the risk of adverse health outcomes often use baseline measures of weight and BMI. Several investigators have suggested that weight gain based on weight measures at both baseline and study completion could be a better approach for evaluating the relationship of obesity and adverse outcomes because it would reflect changes over time rather than rely on a single point estimate of weight. To evaluate these relationships, the umbrella review process was used to identify relevant systematic reviews published during or after 2017 (see Appendix J for summaries of extracted data and Appendix E for eligibility criteria).
Weight Gain
Evidence from systematic reviews
The committee identified seven systematic reviews on weight gain and the risk of chronic disease; all were based on observational studies (Alharbi et al., 2021; Chan et al., 2019; Hao et al., 2021; Karahalios et al., 2017; Jayedi et al., 2018, 2020; Sun et al., 2021). Quality ratings ranged from “partially well done” to “well done” (see Appendix J).
Weight gain and risk of all-cause mortality was evaluated in two systematic reviews (Alharbi et al., 2021; Karahalios et al., 2017). Given the likelihood that weight gain in middle-aged to older adults is more likely to involve decreases in muscle mass and increases in abdominal adiposity as compared to younger adults, both systematic reviews selected studies with populations of middle-aged or older adults. Karahalios et al. (2017) evaluated weight gain in healthy adults aged 40–65 years. Subgroup analyses of 18 studies in which baseline and follow-up weights were measured rather than self-reported did not find a significant association between weight gain and risk of all-cause-mortality (hazard ratio [HR], 1.04; 95% CI, 0.97–1.12). In other analyses where measured and self-reported weight gain were combined, high heterogeneity of results was explained in part by the inclusion of studies with self-reported weight measures.
Alharbi et al. (2021) evaluated the effect of weight change in community-dwelling adults aged 65 years and older. Studies in which weight gain was either self-reported or measured were combined. The study found a small but significant association between weight gain and all-cause mortality (HR, 1.10; 95% CI, 1.02–1.17).
Weight Gain and CVD
The association between weight gain and CVD mortality was evaluated in two systematic reviews (Jayedi et al., 2020; Karahalios et al., 2017). When meta-analyses were conducted in a subsample of 50- to 65-year-old adults in which baseline and follow-up weights were measured rather than self-reported, investigators found that weight gain was not significantly associated with the risk of CVD mortality (HR, 1.14; 95% CI, 0.97–1.35) (Karahalios et al., 2017). The systematic review by Jayedi et al. (2020) included adults 18 years and older. The included studies contained a mixture of measured and self-reported weight gains. Results of the analysis showed an 11 percent higher risk of CVD mortality associated with a 5-kg weight gain during adulthood (relative risk [RR], 1.11; 95% CI, 1.04–1.19). In subgroup analyses, significant associations were observed only with a follow-up duration of 10 or more years, when participants had a mean age less than 65 years, and with exclusion of participants with preexisting CVD.
Weight gain and CVD incidence was evaluated in two systematic reviews (Jayedi et al., 2020; Sun et al., 2021). Jayedi et al. (2020) included studies of adults 18 years and older. Reported weight gains reflected unintentional increases in weight during adulthood. Two of the selected studies reported on weight gain and the risk of CVD incidence. Results indicated that a 12 percent higher risk of CVD incidence was associated with a 5-kg increment in weight (RR, 1.12; 95% CI, 1.10–1.13). Sun et al.
(2021) evaluated associations between weight changes from childhood to adulthood and the onset of CVD in adulthood. The reference group had normal weight during both childhood and adulthood. The study found no association between weight and the onset of CVD for the group that had excess weight during childhood but normal weight during adulthood (odds ratio [OR], 1.22; 95% CI, 0.92–1.62). Among participants with normal childhood weights but excessive adult weights, the results indicated a significantly increased risk of CVD during adulthood (OR, 2.76; 95% CI, 1.79–4.27). For the group in which excess weights occurred during both childhood and adulthood, the risk of CVD was also significantly increased (OR, 3.04; 95% CI, 1.69–5.46).
Weight Gain and Hypertension
Weight gain and hypertension were evaluated in two systematic reviews (Jayedi et al., 2018; Sun et al., 2021). Jayedi et al. (2018) included studies with participants aged 18 years and older from the general population and had a follow-up duration of more than 1 year. Hypertension was significantly associated with adult weight gain equal to a 1-unit increase in BMI (RR, 1.16; 95% CI, 1.09–1.23). With weight gain, men exhibited a slightly higher incidence of hypertension (RR, 1.20; 95% CI, 1.05–1.36) than women (RR: 1.13; 95% CI, 1.04–1.22). Sun et al. (2021) evaluated associations between childhood to adulthood weight changes and the risk of hypertension. There was no association with hypertension when childhood weight was characterized as excessive and adult weight was normal (OR, 1.25; 95% CI, 0.73–2.13). Normal weight in childhood followed by excessive weight in adulthood, however, was associated with an increased risk in hypertension (OR, 2.69; 95% CI, 2.07–3.49). Excess weight in both childhood and adulthood resulted in a stronger relationship with hypertension (OR, 3.49; 95% CI, 2.21–5.50).
Weight Gain and Cancer
Weight gain and cancer mortality was evaluated in one systematic review (Karahalios et al., 2017). The investigators included studies in which healthy participants were between age 40 and 65 years and follow-up from baseline was at least 5 years. The largest weight gain was compared to a reference group. The association between weight gain and cancer mortality was not significant (HR, 1.04; 95% CI, 0.96–1.13). Higher hazard ratios were observed in studies that used self-reported weight values rather than measured values. It is possible that the duration of the follow-up period may have been too short to detect many cancers.
Weight gain and breast cancer was evaluated in two systematic reviews by Chan et al. (2019) and Hao et al. (2021). The Chan et al. (2019) review evaluated weight gain in 5-kg increments from 18 years to study baseline in both premenopausal and postmenopausal women. Changes in adiposity were assessed by BMI, waist circumference, or waist–hip ratio. There was no association between weight gain and breast cancer risk for premenopausal women (RR, 1.00; 95% CI, 0.97–1.03). The association for postmenopausal women was significant (RR, 1.07; 95% CI, 1.05–1.09). There was a significant association between weight gain per 5-kg increase and estrogen receptors and progesterone receptors (ER+PR+) breast cancers (RR, 1.11; 95% CI, 1.06–1.17), but not with ER–PR– or ER+PR– breast cancers (RR, 1.02; 95% CI, 1.00–1.05 and RR, 0.99; 95% CI, 0.97–1.02, respectively).
When Chan et al. (2019) evaluated associations between use of menopausal hormones and risk of breast cancer among postmenopausal women experiencing weight gains, they observed positive associations between hormone “never use,” “never/former use,” and “ever use” (RR, 1.06, 95% CI, 1.03–1.09; RR, 1.09, 95% CI, 1.07–1.12; and RR, 1.08, 95% CI, 1.16, respectively). There was no association between “current” hormone use and breast cancer (RR, 1.00, 95% CI, 0.98–1.03). Adiposity and weight gain were consistently associated with risk of breast cancer regardless of whether adiposity was measured with BMI, waist circumference, or waist–hip ratio.
Hao et al. (2021) evaluated the association between weight gain and incident breast cancer risk across different menopause stages. A significant association between weight gain and breast cancer risk was not observed for premenopausal women (RR, 1.00; 95% CI, 0.83–1.21), but such risk was observed in postmenopausal women (RR, 1.55; 95% CI, 1.40–1.71). An intake–response association for postmenopausal women for a 5-kg increase in weight gain was significant (RR, 1.08; 95% CI, 1.07–1.09). In comparing highest weight gain to lowest weight gain categories in women after menopause, there was an increased risk of postmenopausal breast cancer (RR, 1.59; 95% CI, 1.23–2.05).
Weight Gain and Diabetes
Sun et al. (2021) investigated weight gain and the risk of type 2 diabetes by comparing associations between weight status in childhood and adulthood. Compared to normal weight during both childhood and adulthood (the reference group), significant associations with risk of type 2 diabetes were observed with all other groups. For the group with excessive weight in childhood but normal weight in adulthood, the OR was 1.37 (95% CI, 1.10–1.70). For the group with normal weight during childhood and excess weight in adulthood, the OR was 3.40 (95% CI,
2.71–4.25). For the group in which excess weight occurred during both childhood and adulthood, the OR was 3.94 (95% CI, 3.05–5.08).
Weight Cycling
Twenty to 55 percent of adults with overweight or obesity have a history of weight cycling, a common occurrence in individuals who seek treatment to lose weight (Rhee, 2017). The range of consequences of weight cycling on health outcomes, however, has yet to be clarified. As discussed below, repeated cycles of weight loss and regain have been shown to promote greater subsequent or future weight gain and this has been hypothesized to occur through the process of adaptive thermogenesis or energy compensation and thus may predispose an individual to greater obesity or increased adiposity as a consequence. Long-term obesity is a concern because of the public health implications, such as predisposition to risk of cardiometabolic health outcomes.
Definition of Weight Cycling
The terminology and definitions used to describe weight cycling vary. Examples include weight cycling, yo-yo dieting, weight fluctuation, and obesity relapse. At present, no standardized definition for weight cycling exists with regard to period of time, number of cycles, and amount of weight change to qualify as a weight cycle. Further, limited evidence exists for the effects of weight cycling on energy expenditure in humans. The committees’ search for relevant evidence found no systematic reviews, except for one study that used doubly labeled water (DLW) measures and five studies that used indirect calorimetry to determine resting energy expenditure (REE).
Weight Cycling and Health Outcomes
Evidence from systematic reviews
A systematic review by Alharbi et al. (2021) investigated the association between weight cycling and mortality. The review included four studies with 6,901 participants and showed that weight cycling was associated with a 63 percent increased risk for all-cause mortality.
A systematic review by Zou et al. (2021) examined associations between weight cycling and risk of diabetes. The review included 14 studies with 253,766 participants. Across studies, those with diabetes events and weight cycling had a 23 percent increased risk for type 2 diabetes. However, an association between weight cycling and the risk of diabetes was not found among participants with obesity.
Zhang et al. (2019) conducted a systematic review to identify reports of intentional weight loss, weight cycling after intentional weight loss, bariatric surgery, and endometrial cancer risk. The review included four studies of weight cycling with 92,063 participants among which 3,485 cases of endometrial cancer occurred. Among the four studies, weight cycling was reported to be associated with between 1.23 and 2.33 times increased risks for endometrial cancer.
Zou et al. (2019) evaluated associations between body weight fluctuation and risk of mortality and CVD in a systematic review of 23 studies with 441,199 participants. The review found that weight cycling increased risk for all-cause mortality by 41 percent and risk for CVD mortality by 36 percent.
Evidence from peer-reviewed published literature
El Ghoch et al. (2018) examined the effect of intentional cycling with weight loss and weight regain on energy expenditure, body composition, cardiovascular risk factors, and psychosocial variables in patients with severe obesity. Clinical and psychosocial variables were measured in 38 adults with class III obesity. Participants in the study had been readmitted to a residential treatment program for severe obesity after a cycle of weight loss and regain compared with those logged at a prior admission. The study found no significant changes in REE between the readmitted and prior admission groups. Younger participants and participants with higher historical weight were found to be more likely to regain additional weight.
Fothergill et al. (2016) examined participants from “The Biggest Loser” competition for long-term changes in resting metabolic rate (RMR) and body composition related to weight cycling. RMR and body composition measurements were ascertained from dual energy X-ray absorptiometry during a 3-day inpatient stay. RMR was determined at three points: baseline, following the 30-week Biggest Loser competition, and 6 years after the competition. REE remained significantly below baseline by 704 ± 427 SD kcal/d; total energy expenditure (TEE) by DLW also remained significantly reduced, by 499 ± 207 kcal/d. Although participants experienced significant overall weight regain in the 6 years following the weight loss competition, their RMR remained suppressed at the same average level found at the end of the competition.
Another study of weight cyclers examined the effect of intentional weight loss and obesity classes I and II. Weight-cycling and weight-stable participants were examined at baseline, immediately following weight loss, and at 6 months of follow-up. The study found that weight regain was incomplete and accounted for 83 and 42 percent of total weight loss in female and male participants, respectively. Additionally, regain in total fat and adipose tissue depots was proportional to weight regain, except
for a higher regain in extremity and a lower regain in extremity and visceral adipose tissue in female and male participants, respectively. Overall, REE (adjusted for organ and tissue masses) was significantly reduced in weight-cycling compared to weight-stable participants, suggesting that weight loss–associated adaptations in REE could negatively affect weight loss as well as contribute to weight regain (Bosy-Westphal et al., 2013).
Intentional Weight Loss and Chronic Disease Outcomes
Evidence from Systematic Reviews
Ma et al. (2017) conducted a systematic review of randomized controlled trials to examine the effect of intentional weight reduction on all-cause, cardiovascular, and cancer mortality; CVD; cancer; and body weight for adults with obesity. The study found high-quality evidence that intentional weight reduction in adults with obesity was associated with an 18 percent relative reduction in premature mortality over a median trial duration of 2 years. In addition, the investigators identified evidence indicating that physical activity as an adjunct to weight reduction could enhance the effectiveness of dietary intervention.
LeBlanc et al. (2018) reviewed the evidence on the benefits and harms associated with behavioral and therapeutic weight loss and weight loss maintenance interventions in adults. The study found that, compared to control conditions, behavior-based interventions were associated with more weight loss and that maintenance interventions were associated with less weight regain over the study periods of 12 to 18 months.
Authoritative Reports on Obesity and Chronic Disease Risk
In addition to the scientific literature, numerous agencies have published reports on the association between obesity and the risk of chronic disease. These reports have found that obesity is associated with an increased risk of some cancers, diabetes, and cardiovascular disease (EFSA, 2013; SACN, 2011; Powell-Wiley et al., 2021; WCRF/AICR, 2018; Lauby-Secretan et al., 2016; WHO, 2016b, 2021, 2022).
CONSIDERATIONS FOR INDIVIDUALS WITH SPECIAL NEEDS
Information obtained from DLW databases used in this report does not include data from individuals with acute or chronic diseases; those with critical illnesses such as burns or sepsis or those on mechanical ventilation; those undergoing bariatric procedures; those taking medications that alter energy requirements; or those with physical activity levels greater
than 2.5. Therefore, the EER equations developed do not take into account estimates of the additional energy that may be expended or stored under these conditions. In cases such as these, modification of the EER equation (based on an individual’s sex, age, height, weight, and PAL) or use of an alternative prediction equation would be needed for the individual to optimize accuracy in determining energy requirements. This is especially important because long-term energy imbalance, whether positive or negative, can lead to adverse outcomes ranging from comorbidities to mortality.
Considerations When Applying the EER May Overestimate Needs
Bariatric Surgery
A systematic review of 30 studies that included 1,233 patients showed that REE and TEE were significantly reduced from baseline as late as 12 months after bariatric surgeries. Notably, this review also showed that REE prediction equations overestimated REE after weight loss (Li et al., 2019).
Medications
Evidence from systematic reviews
Whereas the effects of many medications on energy expenditure are unknown, a systematic review of 33 studies showed that continuous sedation or analgesia used in intensive care reduces energy expenditure as measured by indirect calorimetry (Dickerson and Roth-Yousey, 2005). Acute or chronic administration of cardiovascularadrenergic receptor antagonism agents, such as propranolol and atenolol, have also been shown to reduce REE by as much as 12 percent. As body fat depots can affect the systemic distribution of pharmaceutical agents, persons with excess adiposity may experience different effects.
A systematic review of 16 studies with a total of 267 patients assessed the effect of chemotherapy on REE measured by indirect calorimetry (Van Soom et al., 2020). The findings confirmed underestimation of REE with use of the Harris-Benedict equation for all cancer types studied. A significant decrease in REE was shown in lung cancer and non-Hodgkin’s lymphoma. Regardless of type or stage of cancer or chemotherapeutic agent, REE showed a U-shaped curve with an increase upon start of treatment, decrease during treatment, and increase at treatment end.
Evidence from peer-reviewed published literature
A variety of endocrine and cardiometabolic agents have been assessed for effects on energy expenditure, but mechanisms driving effects on energy balance remain unclear. Antihyperglycemic agents used in treatment of type 2 diabetes
can promote weight gain or loss (Apovian et al., 2019). While all types of insulin therapy are associated with weight gain, the effects differ by drug and regimen. Weight gain is also common with newer antiretroviral regimens for treatment of human immunodeficiency virus (HIV) infection, such as integrase strand transfer inhibitors (Bourgi et al., 2020).
Potential for Underestimating Energy Needs
As energy intake in excess of expenditure is increasingly occurring in the general population, undernutrition or inadequate energy intake remains a problem in some population subgroups. A chronic state of energy deficit promotes mobilization of energy stores, resulting in the loss of body mass and altered body composition. The effects of chronic undernutrition include low growth rate (stunting) and impaired bone accretion in children, susceptibility to infections, immune system vulnerability, and impaired wound healing.
Critical Illness
A systematic review of 103 articles that included 4,388 adults, children, and neonates showed that several physiological and clinical factors influencing energy expenditure are not included in predictive equations. Among critically ill hospitalized patients, energy imbalance is associated with mortality (McClave et al., 2016). Further, more accurate estimation of energy requirements can be measured by indirect calorimetry rather than by using the EER prediction equations (Boullata et al., 2007; Oshima et al., 2016).
Protein–Energy Malnutrition States
Similar to critical illness, in several other biological states where protein energy wasting or hypercatabolism occur—such as chronic kidney disease, advanced stage cancer, untreated HIV, chronic obstructive pulmonary disease, and congestive heart failure—the loss of energy stores leads to cachexia, a metabolic syndrome characterized by loss of muscle mass with or without loss of fat mass (Evans et al., 2008). Further, loss of muscle mass combined with loss of muscle strength and performance (power and function), termed sarcopenia, may also alter energy requirements. Sarcopenia is associated with aging and disease processes and is also widely prevalent in the state of obesity in most life stages. A systematic review of 18 studies showed a prevalence of sarcopenic obesity in 29 to 33 percent of boys and 20 to 39 percent of girls aged 6 to 19 years (Zembura and Matusik, 2022).
Individuals with Extremely High Physical Activity Levels
For the general population, a physical activity level (PAL) value of 2.5 represents the upper limit of sustained metabolic rate over periods sufficiently long that body mass remains constant. Athletes (those engaged in intense, extreme endurance activities) can obtain higher sustainable metabolic rates or PAL values during endurance events through strenuous training, consumption of large quantities of food, and capacity to process nutrients (Westerterp, 2001). While many studies show that differences in REE in very active individuals disappear when accounting for fat-free mass, the metabolic response to exercise varies. Thus, it is likely that these individuals will have higher TEEs. For example, the training regimen for many athletes undergoing heavy training consists of frequent periods of high-intensity exertion. A systematic review of 82 studies of 1,674 endurance athletes engaged in various sports showed TEE (measured by DLW or heart rate monitoring or accelerometry) was higher during the competition period than the preparation for competition period and an energy deficit was observed in both periods with TEE higher than energy intake (Heydenreich et al., 2017). Because energy intake also varied by the time period of training, recommendations to meet energy requirements would need to be adapted according to training or seasonal phases.
FINDINGS AND CONCLUSIONS
BMI and Health Outcomes
Findings
The committee finds that systematic reviews show that high BMI (in the WHO categories of overweight and obese) is associated with significantly increased risk for gestational diabetes, juvenile onset type 1 diabetes, childhood/adolescent asthma, prediabetes, type 2 diabetes, hypertension, stroke, premature myocardial infarct, coronary heart disease, several types of cancer, nonalcoholic fatty liver disease, age-related disability, and all-cause mortality, but lower risk of hip fracture.
Conclusions
The committee concludes that the body of evidence based on systematic reviews, meta-analysis, and mendelian randomization indicate a profound relationship between high BMI and functional disabilities, impaired quality of life, serious chronic disease states, and mortality. Limitations in the evidence reviewed by the committee support the need to understand the relationship between metabolically active tissues and organs, as well as the role of
ectopic fat in lean tissues and organs, on energy expenditure. This information is critical given the widespread prevalence of sarcopenic obesity in the general population and the growing awareness that obesity with sarcopenia may affect energy expenditure differently than obesity without sarcopenia.
Weight Cycling and Health Outcomes
Findings
The committee finds that the evidence suggests that while mechanisms are unclear in terms of how weight cycling affects biological and physiological adaptations to body weight and affects health outcomes, concerns persist for long-term deleterious cardiometabolic health consequences. This is particularly true for outcomes known to be associated with obesity, such as type 2 diabetes, certain cancers, and both all-cause and CVD mortality.
Conclusions
The committee concludes that, considered collectively, the systematic reviews on weight cycling and health outcomes examined by the committee suggest that weight cycling appears to reduce REE in persons with obesity at baseline, especially in those who have severe obesity and those who cycle more frequently. However, these data are limited by small sample sizes, lack of standardized definition, and lack of more rigorous study designs such as DLW or metabolic chamber studies.
The committee concludes that the results of the systematic reviews on weight cycling and health outcomes support a need for consensus on a standard definition to improve interpretation, draw conclusions, and make applications from future research. There is a further need to include measures of food intake, diet type, diet pattern, and appetite in future research in order to discern the overall effect of weight cycling on energy balance.
Weight Change and Chronic Disease
Findings
The committee finds that systematic reviews of longitudinal studies examining the association between weight gain and chronic disease as well as mortality provided limited evidence of significance for a casual effect of weight change on disease risk and mortality. The systematic review that examined weight change measured from
childhood to adulthood showed highly significant results for CVD incidence, hypertension, and type 2 diabetes when normal weight children became obese in adulthood and when obesity occurred in both childhood and adulthood.
Conclusions
The committee concludes that length of follow-up, removal of preexisting conditions, and if weight is measured are critical issues to reconcile in this body of literature. Given the increasing prevalence of chronic disease and other diet-related risk factors across the U.S. and Canadian populations, evidence is needed on medications that affect energy metabolism. In addition, research is needed on how energy metabolism, especially TEE, are affected by medications and procedures such as bariatric surgery.
REFERENCES
Alharbi, T. A., S. Paudel, D. Gasevic, J. Ryan, R. Freak-Poli, and A. J. Owen. 2021. The association of weight change and all-cause mortality in older adults: A systematic review and meta-analysis. Age and Ageing 50(3):697-704.
Apovian, C. M., J. Okemah, and P. M. O’Neil. 2019. Body weight considerations in the management of type 2 diabetes. Advances in Therapy 36(1):44-58.
Azizpour, Y., A. Delpisheh, Z. Montazeri, K. Sayehmiri, and B. Darabi. 2018. Effect of childhood BMI on asthma: A systematic review and meta-analysis of case-control studies. BMC Pediatrics 18(1):143.
Bosy-Westphal, A., B. Schautz, M. Lagerpusch, M. Pourhassan, W. Braun, K. Goele, M. Heller, C. C. Glüer, and M. J. Müller. 2013. Effect of weight loss and regain on adipose tissue distribution, composition of lean mass and resting energy expenditure in young overweight and obese adults. International Journal of Obesity (2005) 37(10):1371-1377.
Boullata, J., J. Williams, F. Cottrell, L. Hudson, and C. Compher. 2007. Accurate determination of energy needs in hospitalized patients. Journal of the American Dietetic Association 107(3):393-401.
Bourgi, K., C. A. Jenkins, P. F. Rebeiro, F. Palella, R. D. Moore, K. N. Altoff, J. Gill, C. S. Rabkin, S. J. Gange, M. A. Horberg, J. Margolick, J. Li, C. Wong, A. Willig, V. D. Lima, H. Crane, J. Thorne, M. Silverberg, G. Kirk, W. C. Mathews, T. R. Sterling, J. Lake, J. R. Koethe, and North American AIDS Cohort Collaboration on Research and Design. 2020. Weight gain among treatment-naive persons with HIV starting integrase inhibitors compared to non-nucleoside reverse transcriptase inhibitors or protease inhibitors in a large observational cohort in the United States and Canada. Journal of the International AIDS Society 23(4):e25484.
Byun, D., S. Hong, S. Ryu, Y. Nam, H. Jang, Y. Cho, N. Keum, and H. Oh. 2022. Early-life body mass index and risks of breast, endometrial, and ovarian cancers: A dose-response meta-analysis of prospective studies. British Journal of Cancer 126(4):664-672.
Chan, D. S. M., L. Abar, M. Cariolou, N. Nanu, D. C. Greenwood, E. V. Bandera, A. McTiernan, and T. Norat. 2019. World Cancer Research Fund International: Continuous update project-systematic literature review and meta-analysis of observational cohort studies on physical activity, sedentary behavior, adiposity, and weight change and breast cancer risk. Cancer Causes and Control 30(11):1183-1200.
Church, T. S., M. J. LaMonte, C. E. Barlow, and S. N. Blair. 2005. Cardiorespiratory fitness and body mass index as predictors of cardiovascular disease mortality among men with diabetes. Archives of Internal Medicine 165(18):2114-2120.
Darbandi, M., Y. Pasdar, S. Moradi, H. J. J. Mohamed, B. Hamzeh, and Y. Salimi. 2020. Discriminatory capacity of anthropometric indices for cardiovascular disease in adults: A systematic review and meta-analysis. Preventing Chronic Disease 17:E131.
Dickerson, R. N., and L. Roth-Yousey. 2005. Medication effects on metabolic rate: A systematic review (part 1). Journal of the American Dietetic Association 105(5):835-843.
Dugani, S. B., Y. M. Hydoub, A. P. Ayala, R. Reka, T. Nayfeh, J. F. Ding, S. N. McCafferty, M. Alzuabi, M. Farwati, M. H. Murad, A. A. Alsheikh-Ali, and S. Mora. 2021. Risk factors for premature myocardial infarction: A systematic review and meta-analysis of 77 studies. Mayo Clinic Proceedings: Innovations, Quality and Outcomes 5(4):783-794.
EFSA (European Commission, the Panel on Dietetic Products, Nutrition, and Allergies). 2013. Scientific opinion on Dietary Reference Values for energy. EFSA Journal 11(1):3005.
El Ghoch, M., S. Calugi, and R. Dalle Grave. 2018. Weight cycling in adults with severe obesity: A longitudinal study. Nutrition & Dietetics 75(3):256-262.
Evans, W. J., J. E. Morley, J. Argiles, C. Bales, V. Baracos, D. Guttridge, A. Jatoi, K. Kalantar-Zadeh, H. Lochs, G. Mantovani, D. Marks, W. E. Mitch, M. Muscaritoli, A. Najand, P. Ponikowski, F. Rossi Fanelli, M. Schambelan, A. Schols, M. Schuster, D. Thomas, R. Wolfe, and S. D. Anker. 2008. Cachexia: A new definition. Clinical Nutrition 27(6):793-799.
Fothergill, E., J. Guo, L. Howard, J. C. Kerns, N. D. Knuth, R. Brychta, K. Y. Chen, M. C. Skarulis, M. Walter, P. J. Walter, and K. D. Hall. 2016. Persistent metabolic adaptation 6 years after “The Biggest Loser” competition. Obesity (Silver Spring) 24(8):1612-1619.
Gao, J., X. Lin, Y. He, Y. Fu, Y. Wu, J. Liao, Y. Wu, and X. Lian. 2019. The comparison of different obesity indexes and the risk of lung cancer: A meta-analysis of prospective cohort studies. Nutrition and Cancer 71(6):908-921.
Gonzalez, M. C., M. Correia, and S. B. Heymsfield. 2017. A requiem for BMI in the clinical setting. Current Opinion in Clinical Nutrition and Metabolic Care 20(5):314-321.
Gu, D., M. Tang, Y. Wang, H. Cui, M. Zhang, Y. Bai, Z. Zeng, Y. Tan, X. Wang, and B. Zhang. 2022. The causal relationships between extrinsic exposures and risk of prostate cancer: A phenome-wide Mendelian randomization study. Frontiers in Oncology 12.
Hao, Y., M. Jiang, Y. Miao, X. Li, C. Hou, X. Zhang, H. Chen, X. Zhong, and J. Li. 2021. Effect of long-term weight gain on the risk of breast cancer across women’s whole adulthood as well as hormone-changed menopause stages: A systematic review and dose-response meta-analysis. Obesity Research & Clinical Practice 15(5):439-448.
He, W., Q. Li, M. Yang, J. Jiao, X. Ma, Y. Zhou, A. Song, S. B. Heymsfield, S. Zhang, and S. Zhu. 2015. Lower BMI cutoffs to define overweight and obesity in China. Obesity (Silver Spring) 23(3):684-691.
Heydenreich, J., B. Kayser, Y. Schutz, and K. Melzer. 2017. Total energy expenditure, energy intake, and body composition in endurance athletes across the training season: A systematic review. Sports Medicine Open 3(1):8.
Hidayat, K., H. J. Li, and B. M. Shi. 2018. Anthropometric factors and non-Hodgkin’s lymphoma risk: Systematic review and meta-analysis of prospective studies. Critical Reviews in Oncology-Hematology 129:113-123.
Hidayat, K., S. Y. Zou, and B. M. Shi. 2019. The influence of maternal body mass index, maternal diabetes mellitus, and maternal smoking during pregnancy on the risk of childhood-onset type 1 diabetes mellitus in the offspring: Systematic review and meta-analysis of observational studies. Obesity Reviews 20(8):1106-1120.
Ibe, A., and T. C. Smith. 2014. Diabetes in US women on the rise independent of increasing BMI and other risk factors; a trend investigation of serial cross-sections. BMC Public Health 14:954.
Javed, A. A., J. Ma, L. N. Anderson, A. J. Mayhew, H. Y. So, L. E. Griffith, A. Gilsing, and P. Raina. 2022. Age-appropriate BMI cut-points for cardiometabolic health risk: A cross-sectional analysis of the Canadian longitudinal study on aging. International Journal of Obesity (2005) 46(5):1027-1035.
Jayedi, A., A. Rashidy-Pour, M. Khorshidi, and S. Shab-Bidar. 2018. Body mass index, abdominal adiposity, weight gain and risk of developing hypertension: A systematic review and dose-response meta-analysis of more than 2.3 million participants. Obesity Reviews 19(5):654-667.
Jayedi, A., A. Rashidy-Pour, S. Soltani, M. S. Zargar, A. Emadi, and S. Shab-Bidar. 2020. Adult weight gain and the risk of cardiovascular disease: A systematic review and dose-response meta-analysis of prospective cohort studies. European Journal of Clinical Nutrition 74(9):1263-1275.
Jiang, M., Y. Zou, Q. Xin, Y. Cai, Y. Wang, X. Qin, and D. Ma. 2019. Dose-response relationship between body mass index and risks of all-cause mortality and disability among the elderly: A systematic review and meta-analysis. Clinical Nutrition 38(4):1511-1523.
Karahalios, A., D. R. English, and J. A. Simpson. 2017. Change in body size and mortality: A systematic review and meta-analysis. International Journal of Epidemiology 46(2):526-546.
Khadra, D., L. Itani, H. Tannir, D. Kreidieh, D. El Masri, and M. El Ghoch. 2019. Association between sarcopenic obesity and higher risk of type 2 diabetes in adults: A systematic review and meta-analysis. World Journal of Diabetes 10(5):311-323.
Kim, K. S., W. L. Owen, D. Williams, and L. L. Adams-Campbell. 2000. A comparison between BMI and conicity index on predicting coronary heart disease: The Framingham Heart Study. Annals of Epidemiology 10(7):424-431.
Kim, M. S., W. J. Kim, A. V. Khera, J. Y. Kim, D. K. Yon, S. W. Lee, J. I. Shin, and H. H. Won. 2021. Association between adiposity and cardiovascular outcomes: An umbrella review and meta-analysis of observational and Mendelian randomization studies. European Heart Journal 42(34):3388-3403.
Kitahara, C. M., A. J. Flint, A. Berrington de Gonzalez, L. Bernstein, M. Brotzman, R. J. MacInnis, S. C. Moore, K. Robien, P. S. Rosenberg, P. N. Singh, E. Weiderpass, H. O. Adami, H. Anton-Culver, R. Ballard-Barbash, J. E. Buring, D. M. Freedman, G. E. Fraser, L. E. Beane Freeman, S. M. Gapstur, J. M. Gaziano, G. G. Giles, N. Håkansson, J. A. Hoppin, F. B. Hu, K. Koenig, M. S. Linet, Y. Park, A. V. Patel, M. P. Purdue, C. Schairer, H. D. Sesso, K. Visvanathan, E. White, A. Wolk, A. Zeleniuch-Jacquotte, and P. Hartge. 2014. Association between class III obesity (BMI of 40-59 kg/m2) and mortality: A pooled analysis of 20 prospective studies. PLoS Medicine 11(7):e1001673.
Larsson, S. C., and S. Burgess. 2021. Causal role of high body mass index in multiple chronic diseases: A systematic review and meta-analysis of Mendelian randomization studies. BMC Medicine 19(1):320.
Lauby-Secretan, B., C. Scoccianti, D. Loomis, Y. Grosse, F. Bianchini, K. Straif, and International Agency for Research on Cancer Handbook Working Group. 2016. Body fatness and cancer--viewpoint of the IARC working group. New England Journal of Medicine 375(8):794-798.
LeBlanc, E. S., C. D. Patnode, E. M. Webber, N. Redmond, M. Rushkin, and E. A. O’Connor. 2018. Behavioral and pharmacotherapy weight loss interventions to prevent obesity-related morbidity and mortality in adults updated evidence report and systematic review for the US Preventive Services Task Force. JAMA 320(11):1172-1191.
Li, H., D. Boakye, X. Chen, M. Hoffmeister, and H. Brenner. 2021. Association of body mass index with risk of early-onset colorectal cancer: Systematic review and meta-analysis. American Journal of Gastroenterology 116(11):2173-2183.
Li, K., W. Shi, F. Zhao, C. Yang, Q. Dai, B. Wang, and Y. Li. 2019. Changes in energy expenditure of patients with obesity following bariatric surgery: A systematic review of prospective studies and meta-analysis. Obesity Surgery 29(7):2318-2337.
Li, Z. M., Z. X. Wu, B. Han, Y. Q. Mao, H. L. Chen, S. F. Han, J. L. Xia, and L. S. Wang. 2016. The association between BMI and gallbladder cancer risk: A meta-analysis. Oncotarget 7(28):43669-43679.
Liu, X., Q. Sun, H. Hou, K. Zhu, Q. Wang, H. Liu, Q. Zhang, L. Ji, and D. Li. 2018a. The association between BMI and kidney cancer risk: An updated dose-response meta-analysis in accordance with PRISMA guideline. Medicine (United States) 97(44).
Liu, X., D. Zhang, Y. Liu, X. Sun, Y. Hou, B. Wang, Y. Ren, Y. Zhao, C. Han, C. Cheng, F. Liu, Y. Shi, X. Chen, L. Liu, G. Chen, S. Hong, M. Zhang, and D. Hu. 2018b. A J-shaped relation of BMI and stroke: Systematic review and dose-response meta-analysis of 4.43 million participants. Nutrition, Metabolism and Cardiovascular Diseases 28(11):1092-1099.
Ma, C., A. Avenell, M. Bolland, J. Hudson, F. Stewart, C. Robertson, P. Sharma, C. Fraser, and G. MacLennan. 2017. Effects of weight loss interventions for adults who are obese on mortality, cardiovascular disease, and cancer: Systematic review and meta-analysis. BMJ 359:j4849.
McClave, S. A., B. E. Taylor, R. G. Martindale, M. M. Warren, D. R. Johnson, C. Braunschweig, M. S. McCarthy, E. Davanos, T. W. Rice, G. A. Cresci, J. M. Gervasio, G. S. Sacks, P. R. Roberts, C. Compher, Society of Critical Care Medicine, and American Society for Parenteral and Enteral Nutrition. 2016. Guidelines for the provision and assessment of nutrition support therapy in the adult critically ill patient: Society of critical care medicine (SCCM) and American Society for Parenteral and Enteral Nutrition (A.S.P.E.N.). JPEN Journal of Parenteral and Enteral Nutrition 40(2):159-211.
Meigs, J. B., P. W. Wilson, C. S. Fox, R. S. Vasan, D. M. Nathan, L. M. Sullivan, and R. B. D’Agostino. 2006. Body mass index, metabolic syndrome, and risk of type 2 diabetes or cardiovascular disease. Journal of Clinical Endocrinology and Metabolism 91(8):2906-2912.
Mortensen, S. J., I. Beeram, J. Florance, K. Momenzadeh, A. Mohamadi, E. K. Rodriguez, A. von Keudell, and A. Nazarian. 2021. Modifiable lifestyle factors associated with fragility hip fracture: A systematic review and meta-analysis. Journal of Bone and Mineral Metabolism 39(5):893-902.
Oshima, T., C. P. Heidegger, and C. Pichard. 2016. Supplemental parenteral nutrition is the key to prevent energy deficits in critically ill patients. Nutrition in Clinical Practice 31(4):432-437.
O’Sullivan, D. E., R. L. Sutherland, S. Town, K. Chow, J. Fan, N. Forbes, S. J. Heitman, R. J. Hilsden, and D. R. Brenner. 2021. Risk factors for early-onset colorectal cancer: A systematic review and meta-analysis. Clinical Gastroenterology and Hepatology 20(6):1229-1240, e1225.
Pasco, J. A., G. C. Nicholson, S. L. Brennan, and M. A. Kotowicz. 2012. Prevalence of obesity and the relationship between the body mass index and body fat: Cross-sectional, population-based data. PloS One 7(1):e29580.
Powell-Wiley, T. M., P. Poirier, L. E. Burke, J. P. Despres, P. Gordon-Larsen, C. J. Lavie, S. A. Lear, C. E. Ndumele, I. J. Neeland, P. Sanders, M. P. St-Onge, American Heart Association Council on Lifestyle and Cardiometabolic Health, Council on Cardiovascular and Stroke Nursing, Council on Clinical Cardiology, Council on Epidemiology and Prevention, and Stroke Council. 2021. Obesity and cardiovascular disease: A scientific statement from the American Heart Association. Circulation 143(21):e984-e1010.
Ramuth, H., S. Hunma, V. Ramessur, M. Ramuth, C. Monnard, J. P. Montani, Y. Schutz, N. Joonas, and A. G. Dulloo. 2020. Body composition-derived BMI cut-offs for overweight and obesity in ethnic Indian and Creole urban children of Mauritius. British Journal of Nutrition 124(5):481-492.
Rexrode, K. M., J. E. Buring, and J. E. Manson. 2001. Abdominal and total adiposity and risk of coronary heart disease in men. International Journal of Obesity and Related Metabolic Disorders 25(7):1047-1056.
Rhee, E. J. 2017. Weight cycling and its cardiometabolic impact. Journal of Obesity and Metabolic Syndrome 26(4):237-242.
SACN (Scientific Advisory Committee on Nutrition). 2011. Dietary Reference Values for energy. London, UK: The Stationery Office Limited.
Sharma, V., S. Coleman, J. Nixon, L. Sharples, J. Hamilton-Shield, H. Rutter, and M. Bryant. 2019. A systematic review and meta-analysis estimating the population prevalence of comorbidities in children and adolescents aged 5 to 18 years. Obesity Reviews 20(10):1341-1349.
Sohn, W., H. W. Lee, S. Lee, J. H. Lim, M. W. Lee, C. H. Park, and S. K. Yoon. 2021. Obesity and the risk of primary liver cancer: A systematic review and meta-analysis. Clinical and Molecular Hepatology 27(1):157-174.
Sun, J., B. Xi, L. Yang, M. Zhao, M. Juonala, and C. G. Magnussen. 2021. Weight change from childhood to adulthood and cardiovascular risk factors and outcomes in adulthood: A systematic review of the literature. Obesity Reviews 22(3).
Van Soom, T., S. El Bakkali, N. Gebruers, H. Verbelen, W. Tjalma, and E. van Breda. 2020. The effects of chemotherapy on energy metabolic aspects in cancer patients: A systematic review. Clinical Nutrition 39(6):1863-1877.
WCRF/AICR (World Cancer Research Fund/American Institute for Cancer Research). 2018. Body fatness and weight gain and the risk of cancer. https://www.wcrf.org/wp-content/uploads/2021/01/Body-fatness-and-weight-gain_0.pdf (accessed November 25, 2022).
Weir, C. B., and A. Jan. 2022. BMI classification percentile and cut off points. In Statpearls. Treasure Island (FL). https://www.ncbi.nlm.nih.gov/books/NBK541070/ (accessed December 8, 2022).
Westerterp, K. R. 2001. Limits to sustainable human metabolic rate. Journal of Experimental Biology 204(Pt 18):3183-3187.
WHO (World Health Organization). 2016a. Global database on body mass index. https://www.who.int/data/gho/data/themes/topics/topic-details/GHO/body-mass-index (accessed November 25, 2022).
WHO. 2016b. Global report on diabetes. https://www.who.int/publications/i/item/9789241565257 (accessed November 25, 2022).
WHO. 2021. Obesity and overweight. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight (accessed November 25, 2022).
WHO. 2022. WHO European regional obesity report 2022. Copenhagen, Denmark: World Health Organization, Regional Office for Europe.
Youssef, M. R., A. S. C. Reisner, A. S. Attia, M. H. Hussein, M. Omar, A. LaRussa, C. A. Galvani, M. Aboueisha, M. Abdelgawad, E. A. Toraih, G. W. Randolph, and E. Kandil. 2021. Obesity and the prevention of thyroid cancer: Impact of body mass index and weight change on developing thyroid cancer - pooled results of 24 million cohorts. Oral Oncology 112:105085.
Yu, H. J., M. Ho, X. Liu, J. Yang, P. H. Chau, and D. Y. T. Fong. 2022. Association of weight status and the risks of diabetes in adults: A systematic review and meta-analysis of prospective cohort studies. International Journal of Obesity (2005) 46(6):1101-1113.
Zembura, M., and P. Matusik. 2022. Sarcopenic obesity in children and adolescents: A systematic review. Frontiers in Endocrinology 13:914740.
Zhang, X., J. Rhoades, B. J. Caan, D. E. Cohn, R. Salani, S. Noria, A. A. Suarez, E. D. Paskett, and A. S. Felix. 2019. Intentional weight loss, weight cycling, and endometrial cancer risk: A systematic review and meta-analysis. International Journal of Gynecological Cancer 29(9):1361-1371.
Zhang, Y., C. M. Xiao, Y. Zhang, Q. Chen, X. Q. Zhang, X. F. Li, R. Y. Shao, and Y. M. Gao. 2021. Factors associated with gestational diabetes mellitus: A meta-analysis. Journal of Diabetes Research 2021:6692695.
Zhou, W., Y. Shi, Y. Q. Li, Z. Ping, C. Wang, X. Liu, J. Lu, Z. X. Mao, J. Zhao, L. Yin, D. Zhang, Z. Tian, L. Zhang, and L. Li. 2018. Body mass index, abdominal fatness, and hypertension incidence: A dose-response meta-analysis of prospective studies. Journal of Human Hypertension 32(5):321-333.
Zou, H., P. Yin, L. Liu, W. Liu, Z. Zhang, Y. Yang, W. Li, Q. Zong, and X. Yu. 2019. Body-weight fluctuation was associated with increased risk for cardiovascular disease, all-cause and cardiovascular mortality: A systematic review and meta-analysis. Frontiers in Endocrinology 10:728.
Zou, H., P. Yin, L. Liu, W. Duan, P. Li, Y. Yang, W. Li, Q. Zong, and X. Yu. 2021. Association between weight cycling and risk of developing diabetes in adults: A systematic review and meta-analysis. Journal of Diabetes Investigation 12(4):625-632.
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