Early identification of metabolic syndrome risk: A review of reviews and proposal for defining pre-metabolic syndrome status

Abstract

Aims: a) To analyze the relationship of known and emerging biomarkers/indicators for early risk identification of cardiometabolic health risk; b) to identify early risk markers to be used in both clinical and nonclinical settings; and c) to propose a definition of early risk identification in terms of pre-metabolic syndrome (PreMetSyn). Data synthesis: Pubmed/Medline, Web of Science, Embase, and Cochrane were searched for Systematic Reviews and Meta-analysis. Selected studies were evaluated, and relevant data were extracted and synthesized. Conclusions: Serum uric acid is a good predictive biomarker of metabolic syndrome (MetSyn) and has been associated with non-alcoholic liver fat disease (NAFLD) and type 2 diabetes. NAFLD emerges as an early risk indicator of PreMetSyn by itself. Muscle strength should also be included as an early risk marker of cardiometabolic health. High serum triglycerides and waist circumference confirm their predictive value regarding MetSyn. Indicators related to an inflammatory/proinflammatory status usually linked to MetSyn showed limited evidence as robust biomarkers for PreMetSyn. Authors suggest defining PreMetSyn related to cardiometabolic risk. It is also necessary to determine how close people are to the cut-off point of MetSyn components, including emerging indicators proposed by our review. Some biomarkers could be used as indicators of PreMetSyn, before any of the MetSyn components appear, allowing early health interventions to prevent its development. Defining a PreMetSyn status might consider both emerging indicators and those variables already included in the definition of MetSyn. New indicators should be considered to create a new risk score specifically meant for PreMetSyn.

Eating out of Home: Influence on Nutrition, Health, and Policies: A Scoping Review

“…It was confirmed that variables such as sex, age, income per capita, educational level, total energy intake, smoking and/or drinking status, residential area, and PA were key in developing strategies to prevent metabolic syndrome in different populations [ 40 , 72 ]. Similarly, interventions seeking to reduce energy content through reformulation or diminishing portion sizes in restaurants, cafes, and TA could potentially lead to a decrease in mean daily energy intake [ 23 , 28 , 35 ].…”

Prediction of metabolic and pre-metabolic syndromes using machine learning models with anthropometric, lifestyle, and biochemical factors from a middle-aged population in Korea

“…The Mets group in present study was defined as meeting at least two criteria including both pre-MetS and Mets status, because of the importance of preventive healthcare by early detection of MetS in the middle-aged population [ 33 ]. MetS group was diagnosed by the following five criteria given in the NCEP-ATP III guidelines [ 1 ]: 1) a waist circumference above the cut-off point for Koreans (≥90 cm for males and ≥ 85 cm for females); 2) systolic blood pressure ≥ 130 mmHg, diastolic blood pressure ≥ 85 mmHg, or taking medication for hypertension; 3) a triglyceride level of ≥150 mg/dL or taking medication for such lipid abnormalities; 4) low high-density lipoprotein-cholesterol level (< 40 mg/dL for males and < 50 mg/dL for females) or taking medication for such lipid abnormalities; 5) a fasting plasma glucose level of ≥100 mg/dL or taking medication for type 2 diabetes.…”

Role of platelet indices as diagnostic and predictive biomarkers for comorbidity of diabetes and metabolic syndrome in southern Ethiopia: A comparative cross-sectional study

“…Early prediction of the risk of DM+MetS comorbidity is absolutely important for DM and MetS patient management as well as for CVD prevention, decelerating disease progression, and mortality [10,17]. However, due to the lack of established standard screening methods, the effectiveness and reliability of MetS screening in a DM patient are not convincing, and standards for MetS diagnosis are likely to vary substantially [13,[20][21][22]. In addition, MetS diagnosis and determination need measurements of different parameters at a time that needs sophisticated materials and are easily affected by different factors [13,20].…”

“…Metabolic syndrome and diabetes are pro-inflammatory and prothrombotic disorders characterized by alteration of platelet indices and increased platelet reactivity [23][24][25]. The evidence relates platelet activation (PLT) to MetS and DM [3,4,21,25], however, the tendency to use them in diagnosis is not yet fully evaluated in our context. Platelet indices such as mean platelet volume (MPV), platelet crits (PCT), and platelet distribution width (PDW) are the indicators of increased platelet activity [23,24] and have been identified as potential new biomarkers of disease severity and comorbidity [25].…”