Cardiovascular complications are commonly associated with obesity. However, a subgroup of obese individuals may not be at an increased risk for cardiovascular complications; these individuals are ...said to have metabolically healthy obesity (MHO). In contrast, metabolically unhealthy individuals are at high risk of cardiovascular disease (CVD), irrespective of BMI; thus, this group can include individuals within the normal weight category (BMI 18.5–24.9 kg/m
2
). This review provides a summary of prospective studies on MHO and metabolically unhealthy normal-weight (MUHNW) phenotypes. Notably, there is ongoing dispute surrounding the concept of MHO, including the lack of a uniform definition and the potentially transient nature of metabolic health status. This review highlights the relevance of alternative measures of body fatness, specifically measures of fat distribution, for determining MHO and MUHNW. It also highlights alternative approaches of risk stratification, which account for the continuum of risk in relation to CVD, which is observable for most risk factors. Moreover, studies evaluating the transition from metabolically healthy to unhealthy phenotypes and potential determinants for such conversions are discussed. Finally, the review proposes several strategies for the use of epidemiological research to further inform the current debate on metabolic health and its determination across different stages of body fatness.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Obesity and impaired metabolic health are established risk factors for the non-communicable diseases (NCDs) type 2 diabetes mellitus, cardiovascular disease, neurodegenerative diseases, cancer and ...nonalcoholic fatty liver disease, otherwise known as metabolic associated fatty liver disease (MAFLD). With the worldwide spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), obesity and impaired metabolic health also emerged as important determinants of severe coronavirus disease 2019 (COVID-19). Furthermore, novel findings indicate that specifically visceral obesity and characteristics of impaired metabolic health such as hyperglycaemia, hypertension and subclinical inflammation are associated with a high risk of severe COVID-19. In this Review, we highlight how obesity and impaired metabolic health increase complications and mortality in COVID-19. We also summarize the consequences of SARS-CoV-2 infection for organ function and risk of NCDs. In addition, we discuss data indicating that the COVID-19 pandemic could have serious consequences for the obesity epidemic. As obesity and impaired metabolic health are both accelerators and consequences of severe COVID-19, and might adversely influence the efficacy of COVID-19 vaccines, we propose strategies for the prevention and treatment of obesity and impaired metabolic health on a clinical and population level, particularly while the COVID-19 pandemic is present.
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GEOZS, IJS, IMTLJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZAGLJ
Different methodologic approaches for constructing dietary patterns and differences in their composition limit conclusions on healthful patterns for diabetes prevention.
We summarized evidence from ...prospective studies that examined associations of dietary patterns with type 2 diabetes by considering different methodologic approaches.
The literature search (MEDLINE and Web of Science) identified prospective studies (cohorts or trials) that associated dietary patterns with diabetes incidence in nondiabetic and apparently healthy participants. We summarized evidence by meta-analyses and distinguished different methodologic approaches.
The search resulted in 48 articles comprising 16 cohorts. Adherence to the Mediterranean diet (RR for comparing extreme quantiles: 0.87; 95% CI: 0.82, 0.93), Dietary Approaches to Stop Hypertension (DASH) (RR: 0.81; 95% CI: 0.72, 0.92), and Alternative Healthy Eating Index (AHEI) (RR: 0.79; 95% CI: 0.69, 0.90) was associated with significant risk reductions of incident diabetes. Patterns from exploratory factor and principal component analyses characterized by red and processed meat, refined grains, high-fat dairy, eggs, and fried products ("mainly unhealthy") were positively associated with diabetes (RR: 1.44; 95% CI: 1.27, 1.62), whereas patterns characterized by vegetables, legumes, fruits, poultry, and fish ("mainly healthy") were inversely associated with diabetes (RR: 0.84; 95% CI: 0.77, 0.91). Reduced rank regression (RRR) used diabetes-related biomarkers to identify patterns. These patterns were characterized by high intakes of refined grains, sugar-sweetened soft drinks, and processed meat and were all significantly associated with diabetes risk.
Our meta-analysis suggests that diets according to the Mediterranean diet, DASH, and AHEI have a strong potential for preventing diabetes, although they differ in some particular components. Exploratory dietary patterns were grouped based on concordant food groups and were significantly associated with diabetes risk despite single-component foods having limited evidence for an association. Still, they remain population-specific observations. Consistent positive associations with diabetes risk were observed for 3 RRR patterns.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Among 20 leading global risk factors for years of life lost in 2040, reference forecasts point to three metabolic risks-high blood pressure, high BMI, and high fasting plasma glucose-as being the top ...risk variables. Building upon these and other risk factors, the concept of metabolic health is attracting much attention in the scientific community. It focuses on the aggregation of important risk factors, which allows the identification of subphenotypes, such as people with metabolically unhealthy normal weight or metabolically healthy obesity, who strongly differ in their risk of cardiometabolic diseases. Since 2018, studies that used anthropometrics, metabolic characteristics, and genetics in the setting of cluster analyses proposed novel metabolic subphenotypes among patients at high risk (eg, those with diabetes). The crucial point now is whether these subphenotyping strategies are superior to established cardiometabolic risk stratification methods regarding the prediction, prevention, and treatment of cardiometabolic diseases. In this Review, we carefully address this point and conclude, firstly, regarding cardiometabolic risk stratification, in the general population both the concept of metabolic health and the cluster approaches are not superior to established risk prediction models. However, both subphenotyping approaches might be informative to improve the prediction of cardiometabolic risk in subgroups of individuals, such as those in different BMI categories or people with diabetes. Secondly, the applicability of the concepts by treating physicians and communication of the cardiometabolic risk with patients is easiest using the concept of metabolic health. Finally, the approaches to identify cardiometabolic risk clusters in particular have provided some evidence that they could be used to allocate individuals to specific pathophysiological risk groups, but whether this allocation is helpful for prevention and treatment still needs to be determined.
Individuals with diabetes face higher risks for macro- and microvascular complications than their non-diabetic counterparts. The concept of precision medicine in diabetes aims to optimise treatment ...decisions for individual patients to reduce the risk of major diabetic complications, including cardiovascular outcomes, retinopathy, nephropathy, neuropathy and overall mortality. In this context, prognostic models can be used to estimate an individual’s risk for relevant complications based on individual risk profiles. This review aims to place the concept of prediction modelling into the context of precision prognostics. As opposed to identification of diabetes subsets, the development of prediction models, including the selection of predictors based on their longitudinal association with the outcome of interest and their discriminatory ability, allows estimation of an individual’s absolute risk of complications. As a consequence, such models provide information about potential patient subgroups and their treatment needs. This review provides insight into the methodological issues specifically related to the development and validation of prediction models for diabetes complications. We summarise existing prediction models for macro- and microvascular complications, commonly included predictors, and examples of available validation studies. The review also discusses the potential of non-classical risk markers and omics-based predictors. Finally, it gives insight into the requirements and challenges related to the clinical applications and implementation of developed predictions models to optimise medical decision making.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The purpose of the present literature review was to investigate and summarize the current evidence on associations between dietary patterns and biomarkers of inflammation, as derived from ...epidemiological studies. A systematic literature search was conducted using PubMed, Web of Science, and EMBASE, and a total of 46 studies were included in the review. These studies predominantly applied principal component analysis, factor analysis, reduced rank regression analysis, the Healthy Eating Index, or the Mediterranean Diet Score. No prospective observational study was found. Patterns identified by reduced rank regression as being statistically significantly associated with biomarkers of inflammation were almost all meat‐based or “Western” patterns. Studies using principal component analysis or a priori‐defined diet scores found that meat‐based or “Western‐like” patterns tended to be positively associated with biomarkers of inflammation, predominantly C‐reactive protein, while vegetable‐ and fruit‐based or “healthy” patterns tended to be inversely associated. While results of the studies were inconsistent, interventions with presumed healthy diets resulted in reductions of almost all investigated inflammatory biomarkers. In conclusion, prospective studies are warranted to confirm the reported findings and further analyze associations, particularly by investigating dietary patterns as risk factors for changes in inflammatory markers over time.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Matthias B Schulze and colleagues discuss current knowledge on the associations between dietary patterns and cancer, coronary heart disease, stroke, and type 2 diabetes, focusing on areas of ...uncertainty and future research directions
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BFBNIB, CMK, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
Previous studies have provided inconsistent results about the cardiovascular risks for participants with metabolically healthy obesity (MHO). These uncertainties might partly reflect the lack of a ...uniform definition of MHO. We conducted a systematic review and meta-analysis to examine whether there is a suitable approach that identifies obese participants who are not at an increased risk of cardiovascular events compared with healthy normal-weight participants.
Twenty-two prospective studies were eligible for the meta-analysis. Using random-effect models, pooled relative risks (RRs) were calculated for the combined effects of obesity with the presence or absence of metabolic syndrome, insulin resistance, hypertension, diabetes, hyperlipidaemia and any of these metabolic factors. Participants with MHO defined by the absence of metabolic syndrome were at increased risk for cardiovascular events compared with healthy normal-weight participants (pooled RR 1.45, 95% confidence interval (CI) 1.20-1.70), but had lower risks than unhealthy normal-weight (RR 2.07, 95% CI 1.62-2.65) and obese (RR 2.31, 95% CI 1.99-2.69) participants. The risk associated with participants who had MHO was particularly high over the long term. Similar risk estimates were observed when MHO was defined by other approaches.
None of the approaches clearly identified an obese subgroup not at increased risk of cardiovascular events compared with normal-weight healthy participants. A benign obese phenotype might be defined by strict definitions, but insufficient studies exist to support this. More research is needed to better define MHO.
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