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Adolescent cardiometabolic risk scores: A scoping review

Published:September 05, 2022DOI:https://doi.org/10.1016/j.numecd.2022.08.022

      Highlights

      • No standardized cardiometabolic risk score exists for adolescents.
      • Numerous risk scores have been developed to assess adolescent cardiometabolic risk.
      • Numerous risk factors have been used to assess adolescent cardiometabolic risk.
      • Cardiometabolic risk scores commonly use an internal cohort to create z-scores.
      • Lipid, blood pressure, and adiposity measures are commonly used in risk scores.

      Abstract

      Aims

      Clustering of cardiometabolic risk factors (CMRFs) indicates cardiometabolic risk (CMR), a key driver of cardiovascular disease. Early detection and treatment of CMR are important to decrease this risk. To facilitate the identification of individuals at risk, CMRFs are commonly combined into a CMR Score. This scoping review aims to identify CMRFs and methods used to calculate adolescent CMR Scores.

      Data synthesis

      Systematic searches were executed in Child Development and Adolescent Studies, Ovid MEDLINE, Ovid EMBASE, Ovid PsycINFO, EBSCO CINAHL, Scopus Elsevier, Cochrane CENTRAL, and Nursing and Allied Health. No limits were placed on publication date or geographic location. Studies were included if participants were 10–19 years and the study reported CMRFs in a composite score. Key extracted information included participant characteristics, CMRFs comprising the scores, and methods of score calculation. CMRFs were categorized and data were reported as frequencies. This study identified 170 studies representing 189 CMR Scores. The most common CMRF categories were related to lipids, blood pressure, and adiposity. The most frequent CMRFs were triglyceride z-score, systolic blood pressure z-score, and inverse high-density lipoproteins z-score. Scores were mostly calculated by summing CMRF z-scores without weighting.

      Conclusions

      The range of CMRFs and Scores identified in adolescent CMR literature limits their use and interpretation. Published CMR Scores commonly contain two main limitations: (a) use of an internal cohort as the z-score reference population, and (b) Scores relying on adiposity measures. We highlight the need for a standard set of CMRFs and a consensus for a CMR Score for adolescents.

      Keywords

      Abbreviations:

      CMRFs (Cardiometabolic Risk Factors), CMR (Cardiometabolic Risk), FRS (Framingham Risk Score), CVD (Cardiovascular Disease), SBP (Systolic Blood Pressure), DBP (Diastolic Blood Pressure), HDL (High-Density Lipoproteins), MetS (Metabolic Syndrome), cMetS (Continuous Metabolic Syndrome Score), BD (Bipolar Disorders), MDD (Major Depression Disorder), AHA (American Heart Association), Pathological Determinations of Atherosclerosis in Youth Risk Score (PDAY Risk Score)

      1. Introduction

      Accumulation of cardiometabolic risk factors (CMRFs) contributes to cardiometabolic risk (CMR) and increases the risk of cardiometabolic disease and future cardiovascular events such as ischemic heart disease, myocardial infarction, and valvular disease. Cardiometabolic risk encompasses the chance of cardiovascular events and diabetes mellitus occurring [
      • Chatterjee A.
      • Harris S.B.
      • Leiter L.A.
      • Fitchett D.H.
      • Teoh H.
      • Bhattacharyya O.K.
      • et al.
      Managing cardiometabolic risk in primary care: summary of the 2011 consensus statement.
      ]. Therefore, early detection and treatment of CMR in adolescence are important to future adult health. To date, extensive research has been conducted in adults uncovering CMR and related mechanisms [
      • Executive C.
      • Leiter L.A.
      • Fitchett D.H.
      • Gilbert R.E.
      • Gupta M.
      • Mancini G.B.
      • et al.
      Cardiometabolic Risk Working Group
      Cardiometabolic risk in Canada: a detailed analysis and position paper by the cardiometabolic risk working group.
      ]. In adults, the Framingham Risk Score (FRS) is a commonly used scoring system used to detect the 10-year risk of Cardiovascular Disease (CVD) and is used as an early indicator of CMR [
      • Bitton A.
      • Gaziano T.
      The Framingham Heart Study's impact on global risk assessment.
      ]. The FRS is comprised of systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol, high-density lipoproteins (HDL), diabetes mellitus, treatment for hypertension, and smoking [
      • Bitton A.
      • Gaziano T.
      The Framingham Heart Study's impact on global risk assessment.
      ]. FRS is calculated by summing points assigned to each risk factor [
      • Bitton A.
      • Gaziano T.
      The Framingham Heart Study's impact on global risk assessment.
      ]. Cardiometabolic diseases and related events largely manifest in adulthood, however, atherosclerotic processes can begin in childhood [
      • Berenson G.S.
      • Srnivasan S.R.
      • Bogalusa Heart Study G.
      Cardiovascular risk factors in youth with implications for aging: the Bogalusa Heart Study.
      ,
      • Raitakari O.T.
      • Juonala M.
      • Ronnemaa T.
      • Keltikangas-Jarvinen L.
      • Rasanen L.
      • Pietikainen M.
      • et al.
      Cohort profile: the cardiovascular risk in Young Finns Study.
      ].
      Early identification of CMR in children and adolescents is critical to prevent the onset and development of cardiometabolic disease. Yet, research regarding CMR in children and adolescents is scant and no standard CMR Score exists. There is currently no official definition for Metabolic Syndrome (MetS) in adolescents [
      • Serbis A.
      • Giapros V.
      • Galli-Tsinopoulou A.
      • Siomou E.
      Metabolic syndrome in children and adolescents: is there a universally accepted definition? Does it matter?.
      ]. The Continuous Metabolic Syndrome Score (cMetS) has been used to detect children and adolescents with elevated CMR in their cohort [
      • Heshmat R.
      • Heidari M.
      • Ejtahed H.-S.
      • Motlagh M.E.
      • Mahdavi-Gorab A.
      • Ziaodini H.
      • et al.
      Validity of a continuous metabolic syndrome score as an index for modeling metabolic syndrome in children and adolescents: the CASPIAN-V study.
      ]. As there is no official score, a wide spectrum of scoring methods has been designed to assess CMR in children and adolescents.
      Many factors increase an individual's CMR. For example, there is a significant relationship between diet and CVD that begins in youth [
      • Getz G.S.
      • Reardon C.A.
      Nutrition and cardiovascular disease.
      ]. Diet is also related to atherosclerosis and hypertension, two conditions contributing to CVD [
      • Getz G.S.
      • Reardon C.A.
      Nutrition and cardiovascular disease.
      ]. A healthy diet is a key to preventing CVD, the American Heart Association's (AHA) recommendations for diet include consuming an overall healthy diet, eating the recommended levels of low-density lipoproteins, HDLs and TGs, and aiming for normal blood glucose levels [
      • Lichtenstein A.H.
      • Appel L.J.
      • Brands M.
      • Carnethon M.
      • Daniels S.
      • Franch H.A.
      • et al.
      Summary of American Heart Association diet and lifestyle recommendations revision 2006.
      ]. Detecting unhealthy diets among youth could provide a preventative window where diets could be modified, decreasing the risk of future CVD. In addition, psychiatric disorders in childhood and adolescence such as attention deficit hyperactivity disorder, autism spectrum disorder, anxiety disorders, depression, and bipolar disorders (BD) are linked to greater CMR in adulthood independent of body weight or the presence of metabolic syndrome [
      • Goldstein B.I.
      • Korczak D.J.
      Links between child and adolescent psychiatric disorders and cardiovascular risk.
      ]. Youth onset Major Depression Disorder (MDD) and BD have been identified as tier two moderate-risk conditions predisposing youth to premature CVD [
      • Goldstein B.I.
      • Carnethon M.R.
      • Matthews K.A.
      • McIntyre R.S.
      • Miller G.E.
      • Raghuveer G.
      • et al.
      Major depressive disorder and bipolar disorder predispose youth to accelerated atherosclerosis and early cardiovascular disease: a scientific statement from the American Heart Association.
      ]. A tier two moderate-risk condition is defined by the AHA as a disease with “pathophysiological evidence for arterial dysfunction indicative of accelerate atherosclerosis before 30 years of age” [
      • Kavey R.-E.W.
      • Allada V.
      • Daniels S.R.
      • Hayman L.L.
      • McCrindle B.W.
      • Newburger J.W.
      • et al.
      Cardiovascular risk reduction in high-risk pediatric patients.
      ]. AHA Guidelines suggest that adolescents should be considered tier one or high-risk when assessment uncovers two or more adverse risk factors. Treatment goals include lifestyle changes, and drug therapy dependent on the tier of risk [
      • Goldstein B.I.
      • Carnethon M.R.
      • Matthews K.A.
      • McIntyre R.S.
      • Miller G.E.
      • Raghuveer G.
      • et al.
      Major depressive disorder and bipolar disorder predispose youth to accelerated atherosclerosis and early cardiovascular disease: a scientific statement from the American Heart Association.
      ]. A Swiss study among female high school students reported that approximately 71% of the adolescents diagnosed with a psychiatric disorder were normal weight [
      • Buddeberg-Fischer B.
      • Klaghofer R.
      • Reed V.
      Associations between body weight, psychiatric disorders and body image in female adolescents.
      ]. Additionally, elevated CMRFs have been reported in adolescents who are of normal weight but have excess body fat [
      • Cota B.C.
      • Priore S.E.
      • Ribeiro S.A.V.
      • Juvanhol L.L.
      • de Faria E.R.
      • de Faria F.R.
      • et al.
      Cardiometabolic risk in adolescents with normal weight obesity.
      ]. Obesity tracks to adulthood and contributes to CMR [
      • Chung S.T.
      • Onuzuruike A.U.
      • Magge S.N.
      Cardiometabolic risk in obese children.
      ], however, CMR exists in adolescents with psychiatric conditions, and adolescents of normal weight but with excess body fat, regardless of weight status. Thus, a pediatric CMR Score that relies on measures of adiposity could be less effective in detecting CMR in these populations.
      This review aims to identify CMRFs most frequently used in composite CMR Scores for adolescents and to determine, when possible, the association of adolescent composite CMR Scores with future cardiovascular health.

      2. Methods

      This study was a scoping review aimed at synthesizing the literature regarding the adolescent assessment of CMR. Systematic searches were executed in Child Development and Adolescent Studies (EBSCOhost), Ovid MEDLINE (1946-present including Epub ahead of print, in-process, and other unindexed citations), Ovid EMBASE (1947-present), Ovid PsycINFO (1806-present), EBSCO CINAHL Plus with Full Text (1981-present), Scopus (Elsevier), Cochrane CENTRAL and Nursing and Allied Health (ProQuest) on July 23rd, 2021. No date or language limits were imposed. The search strategy (Supplementary Table 1) was developed by RQ and peer-reviewed by an academic health sciences librarian at SickKids Hospital following Peer Review of Electronic Search Strategies (PRESS) for systematic review guidelines [
      • McGowan J.
      • Sampson M.
      • Salzwedel D.M.
      • Cogo E.
      • Foerster V.
      • Lefebvre C.
      PRESS peer review of electronic search Strategies: 2015 guideline statement.
      ]. The scoping review protocol is available at https://osf.io/4txdp [
      • Quinn R.C.S.
      • McCrindle B.
      • Korczak D.
      Cardiometabolic risk composite scores among adolescents: a scoping review protocol.
      ].

      2.1 Inclusion and exclusion criteria

      This review considered peer-reviewed observational, analytic cross-sectional studies, retrospective and prospective cohort studies, case-control studies, randomized control trials, and quasi-experimental studies of adolescent samples with a mean age of 10–19 for inclusion in order to capture all studies using composite CMR Scores during adolescence, as defined by the World Health Organization (WHO). Ecological studies, proportional mortality studies, case-crossover studies, narrative, and systematic reviews were excluded. All settings and geographic regions were included. Due to limited capacity within the study for a translator, only English studies were included. The outcome was CMRFs used in composite scores calculated by any method. Studies that reported CMRFs without a composite score were not eligible for inclusion.

      2.2 Screening and selection of studies

      All references generated by the systematic searches were screened using Covidence online software in duplicate by RQ and SCC, first by abstract and title, followed by full-text review [

      Covidence systematic review software. Available at: www.covidence.org Melbourne, Australia: Veritas Health Innovation; [Available at: www.covidence.org].

      ]. Upon disagreement, two reviewers discussed reasons for inclusion and exclusion to determine if the study should be included or excluded until consensus was reached.

      2.3 Data extraction

      Data were extracted by one reviewer (RQ) onto a data extraction form designed a priori using Microsoft Office Excel [
      • Corporation M.
      Microsoft Corporation. Microsoft Excel [Internet].
      ]. A second reviewer (SCC) extracted and matched 10% of the studies, the remaining studies were verified as part of the data extraction process. Extracted data included study sample characteristics, mean age (when mean age was not reported, median age), sample size, country, measures of obesity, CMRFs included in the CMR Score, the reason for the inclusion of CMRFs, and the analytic method used to calculate the CMR Score. When the CMR Score in adolescence and later was reported, this information was extracted to determine the predictability of the CMR Scores.

      2.4 Data Synthesis

      CMRFs were categorized into 13 categories as follows: adiposity (i.e. body weight, waist circumference), lipids (i.e. HDL, TG), blood pressure (i.e. SBP, diastolic blood pressure), metabolism (i.e. fasting glucose, fasting insulin), cardiorespiratory fitness (i.e. maximal oxygen consumption, Andersen Running Test), adipocytokines and cytokines (i.e. tumor necrosis factor-alpha, c-reactive protein), demographics (i.e. age, sex), endothelial function (i.e. flow-mediated dilation, flow-mediated dilation percentage), autonomic function (i.e. resting heart rate, heart rate recovery time), markers of insulin production (i.e. c-peptide, homeostatic model assessment for insulin resistance), markers of liver cell injury (i.e. log aspartate aminotransferase, log alanine aminotransferase), clotting factors (i.e. fibrinogen, fibrinogen z-score), and risk behaviors (i.e. smoking, diet). All data are reported as frequencies.

      3. Results

      After applying the inclusion criteria, 170 studies representing 189 CMR Scores were included in this scoping review as outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart (Fig. 1). Among the 189 CMR Scores, 123 scores contained unique combinations of CMRFs. A summary table of included studies can be found in Supplementary Table 2. The approximate mean age of participants across all studies was 13.7 years (54% female). The sample size ranged from 20 to 29,734 participants. Geographically, 52.9% studies were from Europe (n = 90), 28.2% were from North America (n = 48), 11.2% were from South America (n = 19), 4.1% were from Asia (n = 7), 1.8% were from Australia (n = 3), 0.6% were from Africa (n = 1), 0.6% used cohorts from Europe and Africa (n = 1), and 0.6% used cohorts from Europe, Australia, North America, and South America (n = 1) (see Fig. 2).
      Fig. 2
      Figure 2CMRF Categories used in CMR Scores. CMRFs in each CMRF category per (colour of individual cells represent the number of times a CMRF was included from the CMRF category). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
      Approximately 74% of studies were cross-sectional studies (n = 125), 15% were longitudinal studies (n = 26), and 7% were prospective cohort studies (n = 11). The remaining types of included studies can be found in Supplementary Table 3.
      This study identified 126 unique CMRFs, grouped into 13 CMRF categories. Approximately 82% (869/1056) of the included CMRFs belonged to four CMRF categories: lipids (n = 387, 37%), blood pressure (n = 213, 20%), adiposity (n = 150, 14%), and metabolism (n = 119, 11%). See Supplementary Table 4 for the ranking of all CMRF categories and Supplementary Tables 6–18 for CMRF ranking for each CMRF category. See Supplementary Table 19 for the ranking of included adiposity measures.
      Among all 13 CMRF categories, the most reported individual CMRF was TG z-score (n = 109), belonging to the lipids category, and making up 10% of the reported CMRFs. The next most reported CMRFs were SBP z-score (n = 92) belonging to the blood pressure category, and (−1) HDL z-score (n = 74) also belonging to the lipids category. A full list of the reported CMRFs and their rankings can be found in Supplementary Table 5.
      Most CMR Scores included six CMRFs; however, the number of included CMRFs ranged from three to 14. Certain scores were used more than once, for example, the Pathological Determinants of Atherosclerosis in Youth (PDAY) Risk Score and cMetS score were used in more than one study.
      The rationale for CMR selection was reported in 84% (n = 158) of scores. The most common reason for CMRF selection, used in 61% of studies, was that other studies recommended the use of the CMRFs to assess CMR (n = 116), followed by the inclusion of MetS components reported in 11% of studies (n = 21). See Supplementary Table 20 for a complete list of reported rationales.

      3.1 Methodological considerations

      Various CMR Scores weighted CMRFs differently. Approximately 94% (n = 177) of CMR Scores weighted each CMRF equally, while 6% (n = 12) of risk scores weighted CMRFs unequally in the calculation of CMR. For example, the PDAY Risk Score [
      • Dzelajlija D.D.
      • Spasic S.S.
      • Kotur-Stevuljevic J.M.
      • Bogavac-Stanojevic N.B.
      Cardiovascular risk factors in 7-13 Years old children from Vojvodina (Serbia).
      ,
      • Waloszek J.M.
      • Byrne M.L.
      • Woods M.J.
      • Nicholas C.L.
      • Bei B.
      • Murray G.
      • et al.
      Early physiological markers of cardiovascular risk in community based adolescents with a depressive disorder.
      ,
      • Sharma S.
      • Denburg M.R.
      • Furth S.L.
      The association between creatinine versus cystatin C-based eGFR and cardiovascular risk in children with chronic kidney disease using a modified PDAY risk score.
      ,
      • McMahan C.A.
      • Gidding S.S.
      • Viikari J.S.A.
      • Juonala M.
      • Kahonen M.
      • Hutri-Kahonen N.
      • et al.
      Association of Pathobiologic Determinants of Atherosclerosis in Youth risk score and 15-year change in risk score with carotid artery intima-media thickness in young adults (from the Cardiovascular Risk in Young Finns Study).
      ,
      • da Costa I.F.A.F.
      • Medeiros C.C.M.
      • Souza D.R.
      • Simoes MOdS.
      • Carvalho D.F.
      • da Costa F.D.A.F.
      • et al.
      Adolescents: behavior and cardiovascular risk.
      ,
      • Patel K.
      • Wang J.
      • Jacobson D.L.
      • Lipshultz S.E.
      • Landy D.C.
      • Geffner M.E.
      • et al.
      Aggregate risk of cardiovascular disease among adolescents perinatally infected with the human immunodeficiency virus.
      ,
      • Ramos T.D.A.
      • Dantas T.M.E.
      • Simoes M.O.S.
      • Carvalho D.F.
      • Medeiros C.C.M.
      Assessment of the carotid artery intima-media complex through ultrasonography and the relationship with Pathobiological Determinants of Atherosclerosis in Youth.
      ], assigned different weights to CMRFs in the calculation of CMR to reflect their association with arterial pathology. Weighting was based on the outcomes of 10-year cardiovascular risk and the risk of atherosclerotic lesions.
      Studies used a variety of methods to calculate adolescent CMR Scores from the measured CMRFs. The most common method reported was the summation of z-scores used in 55% (n = 104) of scores, followed by summing the average z-scores in 29% (n = 55) of scores, both weighted CMRFs equally. Summing the CMRF z-scores yields a value representative of the individual's cumulative CMR, whereas averaging the CMRF z-scores gives a value indicative of the person's average CMRF score. Sum scores are beneficial as they indicate the total CMR. Average scores are beneficial as they indicate if on average an individual's CMRFs are outside the healthy range, however they do not indicate total risk. The summation of points assigned to specific risk factors was used in 8% of scores (n = 15) which sometimes weighted the CMRFs unequally. See Supplementary Table 21 for the full list of methods used to calculate CMR. Z-scores were mostly standardized using the study's cohort. Z-scores were internally derived in 84% (n = 135) of scores and used a reference population in 11% (n = 18) of scores. See Supplementary Table 22 for the full list of methods used to calculate z-scores.
      Other examples of CMR Score algorithms included mathematical transformations of risk factors i.e., reverse coding of variables when the risk factor is understood to have an inverse relationship with CMR in 10% of CMRFs (n = 108), logarithmically in 8% of CMRFs (n = 82), or with a natural logarithm in 2% of CMRFs (n = 17).
      On average CMR Scores used six CMRFs. The most common CMRFs were TG z-score, SBP z-score, inverse HDL z-score, waist circumference z-score, fasting glucose z-score, and homeostatic model assessment for insulin resistance z-score. Most often, z-scores were calculated using the study cohort as a reference population, weighted equally, and summed to create a total CMR score.

      3.2 Association of CMR scores with future cardiometabolic health

      Six studies assessed CMR in adolescence with a follow-up later in life using CMR Scores [
      • McMahan C.A.
      • Gidding S.S.
      • Viikari J.S.A.
      • Juonala M.
      • Kahonen M.
      • Hutri-Kahonen N.
      • et al.
      Association of Pathobiologic Determinants of Atherosclerosis in Youth risk score and 15-year change in risk score with carotid artery intima-media thickness in young adults (from the Cardiovascular Risk in Young Finns Study).
      ,
      • Bao W.
      • Srinivasan S.R.
      • Wattigney W.A.
      • Berenson G.S.
      Persistence of multiple cardiovascular risk clustering related to syndrome X from childhood to young adulthood: the Bogalusa heart study.
      ,
      • Kemper H.C.G.
      • Post G.B.
      • Van Mechelen W.
      • Twisk J.W.R.
      Clustering of risk factors for coronary heart disease: the longitudinal relationship with lifestyle.
      ,
      • Kynde I.
      • Heitmann B.L.
      • Bygbjerg I.C.
      • Andersen L.B.
      • Helge J.W.
      Hypoadiponectinemia in overweight children contributes to a negative metabolic risk profile 6 years later.
      ,
      • Ross K.
      • Martin T.
      • Chen E.
      • Miller G.E.
      • Alderman ABBBBBBBCCD-EDEEEEFFF-SGGGGGHHIJ
      Social encounters in daily life and 2-year changes in metabolic risk factors in young women.
      ,
      • Andersen L.B.
      • Haraldsdottir J.
      Tracking of cardiovascular disease risk factors including maximal oxygen uptake and physical activity from late teenage to adulthood. An 8-year follow-up study.
      ]. Two studies did not report CMR Score at follow-up, though one of these studies, with two years between baseline and follow-up, indicated there had been no change in CMR Score [
      • Ross K.
      • Martin T.
      • Chen E.
      • Miller G.E.
      Social encounters in daily life and 2-year changes in metabolic risk factors in young women.
      ]. Within each study, the same CMRFs and CMR Score calculation method were done at baseline and at follow-up. Since different CMRFs, variable number of CMRFs and methods for calculating CMR we were unable to do a meta-analysis among the remaining 4 studies. Other factors of heterogeneity precluding the ability to meta-analyze the studies were the follow-up duration which ranged from 2 to 15.0 years and mean age at follow-up which ranged from 15.7 years to 33 years. Among the four studies that did report CMR Score at follow-up, two studies found that scores increased [
      • McMahan C.A.
      • Gidding S.S.
      • Viikari J.S.A.
      • Juonala M.
      • Kahonen M.
      • Hutri-Kahonen N.
      • et al.
      Association of Pathobiologic Determinants of Atherosclerosis in Youth risk score and 15-year change in risk score with carotid artery intima-media thickness in young adults (from the Cardiovascular Risk in Young Finns Study).
      ,
      • Andersen L.B.
      • HaraldsdÓTtir J.
      Tracking of cardiovascular disease risk factors including maximal oxygen uptake and physical activity from late teenage to adulthood an 8-year follow-up study.
      ], and two studies found that scores increased in some groups of participants and decreased in other groups of participants from the sample population [
      • Kemper H.C.G.
      • Post G.B.
      • Van Mechelen W.
      • Twisk J.W.R.
      Clustering of risk factors for coronary heart disease: the longitudinal relationship with lifestyle.
      ,
      • Kynde I.
      • Heitmann B.L.
      • Bygbjerg I.C.
      • Andersen L.B.
      • Helge J.W.
      Hypoadiponectinemia in overweight children contributes to a negative metabolic risk profile 6 years later.
      ] (Supplementary Table 23).

      4. Discussion

      This scoping review synthesized research reporting adolescent CMR Scores and found 189 CMR Scores in 170 studies. The majority of studies were from Europe and were cross-sectional studies. On average, adolescent CMR Scores included six CMRFs. The most common CMRF categories included in CMR Scores were lipids, blood pressure, and adiposity, with lipids being the most reported. More than three-quarters of CMR Scores included a measure of adiposity. The most frequently reported individual CMRFs across all 13 categories were TG z-score, SBP z-score, and (−1) HDL z-score. Adolescent CMR Scores were predominately calculated by summing equally weighted CMRF z-scores derived using the study's cohort as the reference population.
      A recent scoping review investigating CMR assessment in early childhood (<10 years of age) reported the top CMRF category used was lipids, followed by measures of blood pressure, metabolism, and adiposity [
      • Kamel M.
      • Smith B.T.
      • Wahi G.
      • Carsley S.
      • Birken C.S.
      • Anderson L.N.
      Continuous cardiometabolic risk score definitions in early childhood: a scoping review.
      ]. This is consistent with the current adolescent scoping review's findings, as we identified lipids were most often incorporated in CMR Scores, followed by measures of blood pressure, adiposity, and measures of metabolism. In both reviews, the same four CMRF categories were used most often, the only difference was that in the childhood review metabolic measures ranked third while in the current review it ranked fourth. Both reviews also found the summation of z-scores to be the most common method for the calculation of CMR.
      Among adults, the gold standard FRS used in the assessment of 10-year cardiovascular risk has been verified among those 30 and 74 years of age [
      • Bitton A.
      • Gaziano T.
      The Framingham Heart Study's impact on global risk assessment.
      ]. The CMRFs in the FRS belong to the following categories: demographics, lipids, blood pressure, and risk behaviours. These categories are consistent with those summarized in the current scoping review with the exception of metabolic measures reported in 11% of CMR Scores.
      One limitation in this body of work is the use of an internal reference to calculate z-scores rather than a standard reference population. Z-scores constructed in this way are poor indications of adolescent CMR because they only show the adolescent's CMR within the study's cohort. It is not an accurate reflection of their CMR compared to the broader adolescent population and impedes the ability to compare z-scores against other studies and adolescent groups [
      • Fischer R.L.
      • Milfont T.
      Standardization in psychological research.
      ]. Standardizing z-scores to a standard reference population is important so that data can be compared across studies to data collected among different groups of individuals [
      • Stavnsbo M.
      • Resaland G.K.
      • Anderssen S.A.
      • Steene-Johannessen J.
      • Domazet S.L.
      • Skrede T.
      • et al.
      Reference values for cardiometabolic risk scores in children and adolescents: suggesting a common standard.
      ]. As well, this increases the ability to compare trends of adolescent CMR [
      • Stavnsbo M.
      • Resaland G.K.
      • Anderssen S.A.
      • Steene-Johannessen J.
      • Domazet S.L.
      • Skrede T.
      • et al.
      Reference values for cardiometabolic risk scores in children and adolescents: suggesting a common standard.
      ]. However, there are currently no widely accepted reference values for cardiometabolic risk factors in adolescents, only suggested reference values exist [
      • Stavnsbo M.
      • Resaland G.K.
      • Anderssen S.A.
      • Steene-Johannessen J.
      • Domazet S.L.
      • Skrede T.
      • et al.
      Reference values for cardiometabolic risk scores in children and adolescents: suggesting a common standard.
      ]. In the current scoping review only 11% of CMR Scores used suggested reference values.
      Our review identified six studies that measured CMR Scores in adolescence with a later follow-up, without an intervention. Although the calculated CMR Scores changed between baseline and follow-up, no cardiovascular outcome was reported at this follow-up time. Additionally, the maximum follow-up duration was 15 years, when participants were a mean of 33 years of age; at this age, it remains unlikely that participants manifest the cumulative negative effects of high CMR Scores to a clinically relevant level [
      • Berenson G.S.
      • Srnivasan S.R.
      • Bogalusa Heart Study G.
      Cardiovascular risk factors in youth with implications for aging: the Bogalusa Heart Study.
      ]. One study reported that a change in PDAY risk score early in life is an important predictor of atherosclerosis in later life by predicting carotid artery intima-media thickness [
      • McMahan C.A.
      • Gidding S.S.
      • Viikari J.S.A.
      • Juonala M.
      • Kahonen M.
      • Hutri-Kahonen N.
      • et al.
      Association of Pathobiologic Determinants of Atherosclerosis in Youth risk score and 15-year change in risk score with carotid artery intima-media thickness in young adults (from the Cardiovascular Risk in Young Finns Study).
      ]. Another study stated that in their study, the best predictor of follow-up Multiple Risk Index Score was the baseline Multiple Risk Index Score [
      • Bao W.
      • Srinivasan S.R.
      • Wattigney W.A.
      • Berenson G.S.
      Persistence of multiple cardiovascular risk clustering related to syndrome X from childhood to young adulthood: the Bogalusa heart study.
      ].
      While obesity, a known CMRF, tracks through to adulthood [
      • Chung S.T.
      • Onuzuruike A.U.
      • Magge S.N.
      Cardiometabolic risk in obese children.
      ], adolescents with mood disorders are at increased CMR despite their non-obese status [
      • Goldstein B.I.
      • Korczak D.J.
      Links between child and adolescent psychiatric disorders and cardiovascular risk.
      ,
      • Goldstein B.I.
      • Blanco C.
      • He J.-P.
      • Merikangas K.
      Correlates of overweight and obesity among adolescents with bipolar disorder in the National Comorbidity Survey–Adolescent Supplement (NCS-A).
      ]. The majority of CMR Scores identified in this review included a measure of adiposity and typically scores included six CMRFs meaning that adiposity measures weigh heavily into the total CMR Score. Therefore, CMR Scores including adiposity measures are possibly unable to readily detect CMR in non-overweight adolescents. Although adiposity from adolescence is a key driver of CMR [
      • Must A.
      • Jacques P.F.
      • Dallal G.E.
      • Bajema C.J.
      • Dietz W.H.
      Long-term morbidity and mortality of overweight adolescents: a follow-up of the Harvard Growth Study of 1922 to 1935.
      ], not all adolescents at elevated CMR are overweight such as individuals with MDD. Early detection of risk and intervention is key to preventing the development of cardiometabolic diseases. ”Having a CMR Score that weighs heavily on adiposity may not enable detection of elevated CMR in individuals of healthy body weight, such as in depressed individuals.” Thus, a CMR score sensitive to non-obese adolescents could increase a potential preventative window of intervention for youth with MDD and BD.
      A healthy diet decreases the risk of developing CVD [
      • Lichtenstein A.H.
      • Appel L.J.
      • Brands M.
      • Carnethon M.
      • Daniels S.
      • Franch H.A.
      • et al.
      Summary of American Heart Association diet and lifestyle recommendations revision 2006.
      ]. For example, a diet high in number-6 polyunsaturated fatty acids lowers the risk of atherosclerosis-related CVD [
      • Getz G.S.
      • Reardon C.A.
      Nutrition and cardiovascular disease.
      ]. Low-density lipoprotein concentration is most linked with CVD risk, and HDL concentration is inversely linked with CVD risk [
      • NCEPEPo Detection
      • ToHBCi Adults
      Third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III).
      ]. Many of the identified CMR Scores included CMRFs related to diet, such as HDL levels. Using standardized CMRFs and a standard CMR Score could detect adolescents with unhealthy diets, and provide an opportunity for adolescents to modify their diet. A healthy diet in youth is important as atherogenesis begins early in life and is closely related to diet [
      • Getz G.S.
      • Reardon C.A.
      Nutrition and cardiovascular disease.
      ].
      Current clinical guidelines for cardiovascular health risk reduction in children and adolescents at elevated risk focus on the assessment of individual risk factors using tier-specific cut-points [
      Expert Panel on Integrated Guidelines for Cardiovascular H, Risk Reduction in C, Adolescents
      National Heart L, Blood I. Expert panel on integrated guidelines for cardiovascular health and risk reduction in children and adolescents: summary report.
      ]. The current study presents a myriad of CMR Scores reported in the literature to identify adolescents at high risk. A standardized CMR Score with a core set of CMRFs would be clinically beneficial to detect adolescents at increased CMR, and not just at increased CMR compared with their cohort. As well, a uniform and standard score would allow for results from different studies to be compared and give CMR Scores merit beyond the context of the specific study it was used in. Without a validated CMR Score, it is unknown if the various CMR Scores being used in the literature give an accurate score that represents the individual's CMR. The development of a standardized CMR Score should be sensitive to non-obese adolescents. Future research might consider developing a standard CMR Score and developing standard reference values for CMRFs using pooled data across multiple cohorts. We recommend using Standard references to calculate z-scores, when possible, for example, WHO growth standards for height, weight, and BMI. The development of a standard set of CMRFs identified using the Delphi Method by international experts would achieve this goal [
      • Crawford M.
      • Wright G.
      Delphi method.
      ]. Once a standard set of CMRFs and methods for CMR Scores are proposed, validation of its predictability using longitudinal cohorts would help inform future prevention interventions.

      4.1 Strengths and limitations

      The main strength of the review lies in the rigor applied to the scoping review methodology. As we were interested in CMRFs and CMR Score methods and not a direct comparison of data across studies, we were able to include a large number of studies addressing a wide spectrum of research. This study also had limitations. As scoping reviews do not assess the risk of bias; bias may exist. Therefore, existing bias in any of the included studies could influence the validity of the extracted data. Consequently, only a summary of the literature is provided without meta-analyses and the overall effect cannot be estimated.

      5. Conclusions

      Among the 170 studies, 123 unique CMR Scores were identified highlighting an excessive amount of heterogeneity in this body of literature. As a result, CMR Scores and CMRFs are limited in their use and interpretation. Published CMR Scores commonly contain two main limitations: (a) the use of an internal cohort as the CMF z-score reference population, and (b) CMR Scores relying on a measure of adiposity, potentially being unable to detect CMR in non-obese adolescents. To effectively target adolescent interventions and prevent cardiometabolic disease later in life, a standard CMR Score using a prescribed set of CMRFs is needed.

      Funding

      Rebecka Quinn was funded by the SickKids SSuRe Program . Susan C Campisi is supported by the SickKids RestraComp Postdoctoral Award. This study was supported by funding from the Canadian Institutes of Health Research (CIHR) grant number 409491 .

      Author contributions

      RQ and SCC conceptualized the study design and analysis plan, conducted the analysis, wrote and revised all drafts of the manuscript. BM and DJK provided senior supervision for all aspects of the study. All authors assisted in data interpretation, critically reviewed, and approved the final manuscript.

      Data availability statement

      Reasonable requests for data can be made to Daphne J. Korczak, 1145 Burton Wing, Department of Psychiatry, Hospital for Sick Children, 555 University Avenue, Toronto, Ontario, Canada M5G 1X8; Tel 416 813-6936; Fax 416-813-5236; email [email protected] .

      Declaration of competing interest

      No potential conflict of interest was reported by the author(s).

      Acknowledgements

      The authors would like to acknowledge Simone Holligan and Kaitlyn Mackenzie who assisted with the full-text screening. The authors would also like to acknowledge Ronda Lo for creating select Figures.

      Appendix A. Supplementary data

      The following are the Supplementary data to this article:

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