Evidence map›Paper›PMID 37156256›Full record

ReviewThe lancet. Diabetes & endocrinology2023

Metabolic health and cardiometabolic risk clusters: implications for prediction, prevention, and treatment.

Norbert Stefan, Matthias B Schulze

Abstract readReview
PubMed Publisher
In one paragraph

Review in The lancet. Diabetes & endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 127 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
127citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

127 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  14. Covert macrovascular disease and early outcome after ischemic cerebrovascular events.Clinical research in cardiology : official journal of the German Cardiac Society · 2026
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67 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Norbert StefanDepartment of Internal Medicine IV, University Hospital Tübingen, Tübingen, Germany; Institute of Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich, Tübingen, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany. Electronic address: norbert.stefan@med.uni-tuebingen.de.
Matthias B SchulzeGerman Center for Diabetes Research (DZD), Neuherberg, Germany; Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Cardiovascular DiseasesDiabetes MellitusHypertensionMetabolic SyndromeBody Mass IndexHumansObesityRisk Factors

Identifiers

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.