Associations between handgrip strength and markers of insulin resistance and inflammation in childhood and adolescence: A systematic review with meta-analysis

Associations between handgrip strength and markers of insulin resistance and inflammation in childhood and adolescence: A systematic review with meta-analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Associations between handgrip strength and markers of insulin resistance and inflammation in childhood and adolescence: A systematic review with meta-analysis Takashi Abe, Ricardo B. Viana, Akemi Abe, Shuichi Machida, Hisashi Naito, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6291913/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Research on the association between changes in handgrip strength (HGS) and risk factors for lifestyle-related diseases in children and adolescents is essential to clarify the inverse association between HGS and morbidity/mortality mechanisms. This systematic review with meta-analysis aimed to investigate the cross-sectional and longitudinal associations between HGS and markers of insulin resistance and inflammation in children and adolescents. Observational studies that investigated the cross-sectional or/and longitudinal associations between HGS and markers of insulin resistance and inflammation in children and adolescents were searched. Summary effect size measures were calculated using a random-effects model estimation and reported as Fisher’s r-to-z transformed correlation coefficients and 95% confidence intervals. Fifteen studies (12 cross-sectional, two cross-sectional and longitudinal, and one longitudinal) were included in the systematic review, of which 11 studies were also included in the meta-analyses for cross-sectional correlation. Relative (per body mass) but not absolute HGS was significantly associated (very low evidence) with markers of insulin resistance. Relative HGS was also significantly associated (very low evidence) with most of the inflammatory markers investigated. The three longitudinal studies included had insufficient information to perform a meta-analysis. The results from cross-sectional studies indicated the association (very low evidence) between HGS and several markers of insulin resistance and inflammation existed when studies utilized the relative HGS per body mass. However, no significant relationship was found when studies used absolute HGS. Furthermore, as longitudinal studies were limited, future longitudinal follow-up studies are an important means of resolving these issues. grip strength biomarkers c-reactive protein glucose metabolism pediatrics Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Large-scale longitudinal studies in middle-aged and older adults have repeatedly reported inverse associations between handgrip strength (HGS) and the risk of diabetes [ 1 , 2 ], heart disease [ 3 , 4 ], cancer [ 5 , 6 ], dementia [ 7 , 8 ], and falls [ 9 ]. These associations remain even when adjusting for age, education level, body mass index, alcohol, tobacco, medical history, and others. Genetic [ 10 ] and nongenetic [ 11 ] factors have been proposed to explain these associations. However, what mechanisms explain the inverse association between HGS and morbidity/mortality remains unclear. Although HGS is a biomarker [ 12 ], whether it can improve morbidity and mortality when increased by environmental factors such as sports and exercise training has also yet to be demonstrated [ 13 , 14 ]. Our recent studies revealed that HGS may increase through select sports (i.e., whether or not an athlete plays with sports equipment in their hands) during the period of development [ 15 ], and it is possible to affect HGS in young adulthood [ 16 , 17 ]). The importance of HGS levels acquired during the developmental period is understandable, given that HGS, determined in early adulthood, changes significantly only when age-related decline or injury/disease occurs [ 18 , 19 ]. Furthermore, the HGS acquired during development may be associated with protection or resistance to developing lifestyle-related disease risk factors, influencing morbidity and mortality throughout life. A follow-up study reported inverse associations between baseline HGS and changes in inflammation markers in older women and speculated that inflammation markers partly explained the association between HGS and mortality [ 20 ]. A systematic review and meta-analysis recently reported that higher levels of circulating inflammatory markers are significantly associated with lower muscle strength, including HGS in adults (≥ 18 years) [ 21 ]. However, whether similar associations between HGS and risk factors of health-related diseases exist in children and adolescents is unclear. Thus, this systematic review with meta-analysis investigated the cross-sectional and longitudinal associations between HGS and markers of insulin resistance and inflammation in children and adolescents using baseline values and/or change scores. Our hypotheses are that (i) there would be significant cross-sectional associations between the HGS and insulin resistance and inflammatory markers, and (ii) there would be significant longitudinal associations between changes in HGS and changes in insulin resistance and inflammatory markers. Methods We performed this systematic review according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement [ 22 ]. The study was pre-registered (February 3, 2024) in the International Prospective Register of Systematic Review (PROSPERO) (CRD42024502179). Search strategy English-language searches of the electronic databases Medical Literature Analysis and Retrieval System Online (MEDLINE/PubMed), Scopus, Web of Science, Excerpta Medica Database (Embase), and Cochrane Central Register of Controlled Trials (CENTRAL) were run by two independent researchers (T.A. and R.V). Articles were retrieved from electronic databases combining the following terms: (handgrip strength OR grip strength OR grip) AND (resistin or insulin resistance or insulin sensitivity or inflammation or cytokines OR acute phase proteins) AND (child or children or ten or teenager or pediatric or adolescents or adolescence or juvenile). Supplementary Material 1 shows the completed search strategy. Eligibility criteria Observational studies examining the association between insulin resistance or inflammatory markers and HGS as primary or secondary aim through any type of measurement and collected during childhood age (< 18 years) were included. Studies were excluded based on the following file types: study protocols, conference papers, letters to the editor, books, book sections, theses, film/broadcasts, case studies/reports, opinion articles, abstracts, or reviews. Rayyan software was used independently by two researchers (T.A. and R.V.) to remove duplicates and apply the eligibility criteria with disagreements resolved by a consensus between both researchers. Data extraction The following study characteristics were extracted: authors, publication year, country, study design (cross-sectional or longitudinal [cohort or case-control]), participant characteristics (age, body mass, height, body mass index, sex, and study sample), sample size, insulin resistance markers, inflammatory markers, HGS, effect size, technique used to measure insulin resistance, inflammation, and HGS, and information pertaining to methodological quality. In the event that the same participants are included across multiple articles, the study with the largest sample size and most comprehensive data extraction information was selected. If the same participants are included across multiple articles but the available data are from different outcomes [e.g., fasting glucose, fasting insulin, homeostatic model assessment for insulin resistance (HOMA-IR), and quantitative insulin sensitivity check index (QUICKI)] both effect sizes were extracted and examined in separate meta-analyses. If a study analyzed the relationship between HGS and more than one insulin resistance marker or inflammatory marker concurrently, effect sizes for each association were calculated. These data were extracted independently by two researchers (T.A. and R.V.) with disagreements resolved by a consensus between both researchers. Study quality assessment Methodological quality of the included studies was assessed using the Joanna Briggs Critical Appraisal Tool [ 23 ]. Studies were assessed on an 8-point (cross-sectional) or 10-point (case-control) or 11-point (cohort) scale. Each criterion was coded as “Yes (1), “No” (0), “Unclear” (0) or “Not Applicable” and a proportion of total quality assessment score for each study was calculated based on the total number of items applicable to the study. Studies with a score higher than 70% were classified as having a high quality, those with a score between 50% and 70% as having a medium quality, and those with a score less than 50% as having a low quality. Two researchers (R.V. and T.A.) assessed the methodological quality. Certainty of evidence assessment Based on the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) method, one author (R.V.) rated the certainty for the main comparison and outcome as very low (very uncertainty about the estimate), low (research is very likely to significantly affect our confidence in estimating the effect and is likely to change the estimate), moderate (further research is likely to have an important impact on our confidence in estimating the effect and may change the estimate), or high (further research is very unlikely to change our confidence in estimating the effect) [ 24 ]. Statistical analysis For the cross-sectional studies that only reported standardized beta ( β) coefficients within the range from − 0.50 to 0.50, correlation coefficient ( r) values were calculated by the following equation: r = 0.98 β + 0.05λ [ 25 ]; where λ is an indicator variable that equals 1 when λ is nonnegative and 0 when λ is negative [ 25 ]. After obtaining r values, Fisher’s Z values for each cross-sectional study were calculated from r values by the following equation: [ 26 ]; where ln(x) is the natural logarithm function. Standard error of Fisher’s Z values was calculated the following equation: [ 26 ]. For the cross-sectional studies that reported r values split by sex (boys and girls) or by age (children and youth), it was converted to Fisher’s Z values and combined to a single value (effect), as recommend by Borenstein et al. [ 26 ]. Correlation meta-analyses using Fisher’s r-to-z transformed correlation coefficients were performed to determine the overall correlation between HGS (absolute and relativized by body mass) and insulin resistance and inflammatory markers. For that, Fisher’s Z, and its respective standard error values were pooled under a random effect model. The results, such as the summary effect and its confidence interval, were then converted back to r values for presentation [ 26 ]. The r values were interpreted per Pearson thresholds [ 27 ]: trivial (< 0.10), small (0.10 to < 0.30), moderate (0.30 to < 0.50), and large (≥ 0.5). Random effects model was used to reduce the risk of unknown factors responsible for variability even under homogeneity. Restricted maximum likelihood estimation was used in all models. To improve our results, we conducted several sensitivity analyses (the one study removed method) to consider the influence of each study on the overall results. Due to insufficient longitudinal included studies and available information, it was not possible to pooled data for associations between changes in HGS and the changes in insulin resistance and inflammatory markers. Statistical heterogeneity was assessed using τ 2 , H 2 , Q statistic, and the inconsistency I 2 test. The I 2 statistic estimates the percentage variance between studies and can be roughly interpreted as low (0–40%), moderate (30–60%), substantial (50–90%), or considerable (75–100%) heterogeneity. To note, the I 2 classifications overlap as these are rough guidelines suggested by Higgins et al. [ 28 ]. Publication bias was visually assessed using funnel plots by plotting the effect size of each trial against its standard error. As recommended by Higgins et al. [ 28 ], “Egger’s regression test” was not performed to assess asymmetry of the funnel plot because all between-groups meta-analyses involved less than 10 original studies. All statistical analyses were performed in the Jeffreys’s Amazing Statistics Program (JASP, 0.18.3.0, Netherlands) using an alpha level of p < 0.05 [ 29 ]. All statistical analyses were be performed using an alpha level of p ≤ 0.05. Results Included studies The search strategy retrieved 1,811 records (Embase [ n = 1,057], CENTRAL [ n = 45], MEDLINE [ n = 260], Scopus [ n = 274], and Web of Science [ n = 175]). After duplications were removed ( n = 239), title and abstract of 1,572 records were screened and 1,553 records were eliminated due the following reasons: wrong population (n = 1118), wrong outcome (n = 181), review (n = 154), case report (n = 30), book (n = 28), wrong study, design (n = 21), conference proceedings (n = 10), study protocol (n = 8), book chapter (n = 2), and letter to editor (n = 1). The remaining 19 full-text articles were reviewed further, with four studies excluded due the following reasons: wrong population (n = 3), and no correlation data available for HGS and insulin resistance or inflammatory markers (n = 1) (Supplementary material 2). Thus, 15 studies were included in this systematic review (12 cross-sectional [ 30 – 41 ], two cross-sectional and longitudinal [ 42 , 43 ], and one longitudinal [ 44 ]. As eight [ 33 , 36 , 38 , 39 , 40 – 43 ] of the 14 cross-sectional included studies did not report r values for the cross-sectional correlation between HGS and inflammatory and/or insulin resistance markers, this information was requested for the correspondence authors. However, only four correspondence authors sent the data requested [ 33 , 38 , 41 , 42 ], one author answered that raw data was lost and informed that B coefficients were unstandardized [ 40 ], one author decline our request [ 39 ] and the remaining two authors did not respond our request [ 36 , 43 ]. Thus, the r values for one [ 43 ] of the two studies [ 36 , 43 ] that reported standardized β coefficients within the range from − 0.50 to 0.50 for the cross-sectional analyses were estimated as previously reported in the statistical analysis section. Three studies [ 39 , 40 , 44 ] were excluded from the meta-analysis because it was not possible to estimate r values and one study [ 36 ] was excluded because HGS was relativized by lean mass [Supplementary material 2]. Therefore, 11 of the 15 included studies were also included in the meta-analysis for cross-sectional correlation (eight for insulin resistance [ 32 – 34 , 37 , 38 , 41 – 43 ] and four for inflammation [ 30 , 31 , 34 , 35 ] [Figure 1 ]. The included studies were published from 2011 [ 32 ] up to 2023 [ 36 ] [Table 1 ]. Participant characteristics Participants’ characteristics are summarized in Table 1 . Almost one-third of the included studies (n = 6) were conducted with adolescents [ 30 – 33 , 37 , 40 ], while the other four studies were conducted with children [ 36 , 41 – 43 ], and five were mixed children and adolescents [ 34 , 35 , 38 , 39 , 44 ]. The age range of the samples in many studies was between five and eight years [ 30 – 34 , 37 , 38 , 40 , 43 , 44 ], although the range was narrower for studies of children [ 36 , 41 , 42 ] and wider for mixed children and adolescents [ 35 , 39 ]. The study sample was collected from Europe [ 30 – 33 , 35 – 37 ], North America [ 39 , 40 ], South America [ 34 , 41 ], Asian [ 38 ] countries, and Australia [ 44 ]. Five studies reported the prevalence of overweight and obesity [ 31 , 32 , 34 , 36 , 38 ], while other studies did not clearly describe them. HGS assessment Electronic digital or analog hand dynamometers were used for measuring HGS in 11 studies for meta-analysis [ 30 – 35 , 37 , 38 , 41 – 43 ]. Almost half of these studies (n = 6) did not clearly report which posture (e.g., standing or sitting) was adopted for HGS measurements [ 31 – 33 , 35 , 37 , 41 ], but five other studies performed measurements in the standing position [ 30 , 34 , 38 , 42 , 43 ]. In almost all studies included in the meta-analyses [ 30 – 35 , 37 , 42 , 43 ], the grip span was adjusted to participants' hand size. Meta-analyses: HGS and insulin resistance markers The available data from the included studies [Table 2 ] allowed us to conduct correlation meta-analyses pooling Fisher’s r-to-z transformed correlation coefficients for absolute HGS and fasting glucose, fasting insulin, and HOMA-IR, as well as for relative HGS (relativized by body mass) and these same insulin resistance markers. All meta-analyses pooling Fisher’s r-to-z transformed correlation coefficients are summarized in Table 3 . Fasting glucose Four studies [ 33 , 37 , 38 , 41 ] with four independent comparisons provided data from 5252 children and youth individuals to analyze the relationship between absolute HGS and fasting glucose. There was no significant correlation (k = 4; c = 4; n = 5252; Fisher’s z = 0.004; 95% CI = − 0.06 to 0.07, p = 0.902) between absolute HGS and fasting glucose [Figure 2 A] with considerable heterogeneity (τ 2 = 0.004, I 2 = 80.6%, H 2 = 5.2, Q [ 3 ] = 13.951, p = 0.003). Four studies [ 34 , 37 , 38 , 41 ] with four independent comparisons provided data from 4971 children and youth individuals to analyze the relationship between relative HGS and fasting glucose. There was a significant negative correlation (k = 4; c = 4; n = 4971; Fisher’s z = − 0.06; 95% CI = − 0.09 to − 0.03, p < 0.001) between relative HGS and fasting glucose [Figure 3 A] with low heterogeneity (τ 2 = 0.000004, I 2 = 0.4%, H 2 = 1.0, Q [ 4 ] = 4.422, p = 0.219). Fasting insulin Three studies [ 37 , 38 , 41 ] with three independent comparisons provided data from 4302 children and youth individuals to analyze the relationship between absolute HGS and fasting insulin. There was no significant correlation (k = 3; c = 3; n = 4302; Fisher’s z = 0.0008; 95% CI = − 0.03 to 0.03, p = 0.962) between absolute HGS and fasting glucose [Figure 2 B] with low heterogeneity (τ 2 = 0.0001, I 2 = 13.0%, H 2 = 1.2, Q [ 2 ] = 1.738, p = 0.419). Four studies [ 33 , 37 , 38 , 41 ] with four independent comparisons provided data from 5252 children and youth individuals to analyze the relationship between relative HGS and fasting insulin. There was a significant negative correlation (k = 4; c = 4; n = 5252; Fisher’s z = − 0.23; 95% CI = − 0.31 to − 0.14, p < 0.001) between relative HGS and fasting insulin [Figure 3 B] with considerable heterogeneity (τ 2 = 0.006, I 2 = 87.8%, H 2 = 8.2, Q [ 3 ] = 30.706, p < 0.001). HOMA-IR Five [ 37 , 38 , 41 , 42 , 43 ] studies with five independent comparisons provided data from 6370 children and youth individuals to analyze the relationship between absolute HGS and HOMA-IR. There was no significant correlation (k = 5; c = 5; n = 6370; Fisher’s z = 0.09; 95% CI = − 0.04 to 0.22, p = 0.190) between absolute HGS and HOMA-IR [Figure 2 C] with considerable heterogeneity (τ 2 = 0.021, I 2 = 96.0%, H 2 = 25.1, Q [ 4 ] = 65.144, p < 0.001). Six studies [ 32 , 34 , 37 , 38 , 41 , 42 ] with six independent comparisons provided data from 6192 children and youth individuals to analyze the relationship between relative HGS and HOMA-IR. There was a significant negative correlation (k = 6; c = 6; n = 6192; Fisher’s z = − 0.22; 95% CI = − 0.28 to − 0.15, p < 0.001) between relative HGS and HOMA-IR [Figure 3 C] with considerable heterogeneity (τ 2 = 0.004, I 2 = 80.0%, H 2 = 5.0, Q [ 5 ] = 34.849, p < 0.001). Meta-analyses: HGS and inflammatory markers The available data from the included studies [Table 2 ] allowed us to conduct correlation meta-analyses polling Fisher’s r-to-z transformed correlation coefficients for relative HGS (relativized by body mass) and C-reactive protein (CRP), high-sensitive C-reactive protein (hs-CRP), complement components 3 (C3) and 4 (C4), and interleukin-6 (IL-6) [Table 3 ]. CRP and hs-CRP Two studies [ 31 , 35 ] with two independent comparisons provided data from 1142 children and youth individuals to analyze the relationship between relative HGS and CRP. There was a significant negative correlation (k = 2; c = 2; n = 1142; Fisher’s z = − 0.18; 95% CI = − 0.26 to − 0.10, p < 0.001) between relative HGS and CRP (Fig. 4 A) with moderate heterogeneity (τ 2 = 0.001, I 2 = 44.7%, H 2 = 1.8, Q [ 1 ] = 1.810, p = 0.179). Two studies [ 30 , 34 ] with two independent comparisons provided data from 1198 children and youth individuals to analyze the relationship between relative HGS and hs-CRP. There was a significant negative correlation (k = 2; c = 2; n = 1198; Fisher’s z = − 0.18; 95% CI = − 0.31 to − 0.04, p = 0.013) between relative HGS and hs-CRP [Figure 4 B] with considerable heterogeneity (τ 2 = 0.008, I 2 = 86.6%, H 2 = 5.7, Q [ 1 ] = 5.751, p = 0.016). Complement components 3 (C3) and 4 (C4) Three studies [ 30 , 31 , 35 ] with three independent comparisons provided data from 1671 children and youth individuals to analyze the relationship between relative HGS and C3 and C4. There was a significant negative correlation (k = 3; c = 3; n = 1671; Fisher’s z = − 0.23; 95% CI = − 0.28 to − 0.18, p < 0.001) between relative HGS and C3 [Figure 4 C] with low heterogeneity (τ 2 = 0.000, I 2 = 0.0%, H 2 = 1.0, Q [ 2 ] = 1.613, p = 0.446). There was also a significant negative correlation (k = 3; c = 3; n = 1671; Fisher’s z = − 0.17; 95% CI = − 0.22 to − 0.12, p < 0.001) between relative HGS and C4 [Figure 4 D] with low heterogeneity (τ 2 = 0.000, I 2 = 0.0%, H 2 = 1.0, Q [ 2 ] = 0.920, p = 0.631). IL-6 Two studies [ 30 , 35 ] with two independent comparisons provided data from 1032 children and youth individuals to analyze the relationship between relative HGS and IL-6. There was no significant correlation (k = 2; c = 2; n = 1032; Fisher’s z = − 0.08; 95% CI = − 0.26 to 0.11, p = 0.413) between relative HGS and IL-6 [Figure 4 E] with considerable heterogeneity (τ 2 = 0.016, I 2 = 89.0%, H 2 = 9.1, Q [ 1 ] = 9.114, p = 0.003). Sensitivity analyses A sensitivity analysis (the one study removed method) revealed that significant negative correlations between relative HGS and fasting insulin (all p < 0.001), HOMA-IR (all p < 0.001), C3 (all p < 0.001) and C4 (all p < 0.001) remained after removing each one of the studies included in the main meta-analyses [Supplementary Material 3]. In addition, a significant negative correlation between relative HGS and fasting glucose remained after removing Cohen et al. [ 34 ], Jiménez-Pavón et al. [ 37 ], and López-Gil et al.’s [ 41 ] studies but not after removing Jung et al. [ 38 ] study [Supplementary Material 3]. Sensitive analyses were not performed for correlations between relative HGS and h-CRP or CRP because only two studies were included in their respective main meta-analyses. Subgroup analyses Due to the small number of included studies (k < 10), preplanned subgroup analyses were not performed to test whether the study design (cross-sectional and longitudinal studies) would influence the results. Publication bias Due to the small number of included studies, a visual analysis of the effect sizes (i.e., Fisher’s r-to-z transformed correlation coefficients) of the test results for resistance insulin and inflammatory markers did not indicate the presence or absence of publication bias [Supplementary Material 4]. As reported in the statistical analysis section, “Egger’s regression test” was not performed to assess the asymmetry of the funnel plot because the meta-analyses involved less than 10 original studies [ 28 ]. Study quality All the cross-sectional included studies (n = 12) [ 30 – 41 ] presented a good quality (> 75%) in the Joanna Briggs Critical Appraisal Tool. Most of the cross-sectional included studies (66.7%, n = 8 of 12) [ 30 , 33 – 39 ] reached 7 points (87.5%) in the Joanna Briggs Critical Appraisal Tool, while a quarter of the cross-sectional included studies (n = 3 of 12) [ 32 , 40 , 41 ] reached the highest score possible (8 points, which correspond to 100%) [Supplementary Material 5]. All longitudinal included studies (n = 3) [ 42 – 44 ] reached the six points in the Joanna Briggs Critical Appraisal Tool, which correspond to 66.7% (moderate quality) of the highest possible score (9 points) [Supplementary Material 6]. Certainty of evidence (GRADE) Because the present meta-analysis included only cross-sectional studies, the quality (level) rating of the evidence using GRADE started as low for all primary “outcomes” [Supplementary Material 7]. The evidence was not downgraded by one level for risk of bias (study quality) because all included studies presented a good study quality, as well as the evidence was not downgraded by one level for indirectness because the included studies investigated the same population (children/adolescents). However, for half of the primary outcomes the evidence was downgraded by one level for inconsistency due to substantial/considerable statistical heterogeneity, and for most of the primary outcomes the evidence was downgraded by one level for imprecision because the low limit of the overall effect for the main analysis crossed the clinical threshold for relevance ( r ≥ 0.1). For all primary outcomes the evidence was downgraded by one level for publication bias as visual analysis of the funnel plot did not indicate the presence or absence of publication bias. Therefore, the certainty of our estimates across primary outcomes was evaluated to be very low. Discussion Ample evidence suggests an inverse association between HGS and morbidity and mortality [ 1 – 9 ]. Research on the association between changes in HGS and risk factors for lifestyle-related diseases in children and adolescents seems essential to clarify the mechanisms of the inverse association between HGS and morbidity/mortality [ 45 ]. This research aimed to systematically review and meta-analyze the current evidence for the association between HGS and markers of insulin resistance and inflammation in children and adolescents. The meta-analysis in this review used 11 of the 14 cross-sectional studies. However, insufficient information in the longitudinal studies made it impossible to pool data for associations between HGS change and the changes in insulin resistance and inflammatory markers. Finding from longitudinal studies The results of this review indicate that no longitudinal studies with sufficient research information were found regarding the association between changes in HGS and changes in insulin resistance and inflammatory markers in children and adolescents. Therefore, we could not obtain evidence to elucidate the relationship between the two. However, several lifestyle-related factors, such as sports type [ 15 , 16 ], sleep duration [ 46 ], and diets [ 47 – 50 ], are reported to impact HGS and markers of insulin resistance and inflammation in children and adolescents. A study reported that HGS, when measured in childhood, was associated with prediabetes or type-2 diabetes, similar to HGS measured in young adults and mid-adulthood [ 51 ]. During the developmental period, HGS increases dramatically [ 52 ], and the changes in each individual are not uniform but show clear differences [ 53 ]. Furthermore, the results of the previous study described above [ 51 ] suppose that high HGS acquired during development favorably impacts markers of insulin resistance and may be associated with a lower incidence of diabetes. Therefore, future studies may elucidate the association between changes in HGS and changes in insulin resistance and inflammatory markers in children and adolescents. Finding from cross-sectional studies The results from cross-sectional studies indicated the association between HGS and several markers of insulin resistance and inflammation existed when studies utilized the relative HGS [Figures 3 & 4 ]. However, no significant relationship was found when studies used absolute HGS [Figure 2 ]. The results from each study were adjusted for different factors such as age [ 30 , 31 , 32 , 34 , 38 , 41 , 42 , 43 ], sex [ 30 , 31 , 32 , 34 , 35 , 38 , 41 , 42 , 43 ], pubertal or maturation stage [ 30 , 31 , 32 , 34 , 35 , 37 ], physical activity [ 34 ], BMI or BMI-standard deviation score [ 35 , 37 , 38 , 43 ], school-type [ 42 ], and family background [ 42 , 43 ]. Unlike longitudinal studies that observe changes in HGS, participant's HGS in cross-sectional studies may be determined by complex factors, including being influenced by genetics [ 10 ] and nongenetic factors [ 11 ], including in utero [ 54 , 55 ]. The same may apply to insulin resistance and inflammatory markers. At least a couple of possibilities exist for explaining the observed differences in the association of absolute and relative HGS with insulin resistance and inflammation markers. Firstly, physical and sporting activities may be a possible factor. It is well known that physical and sporting activities impact insulin resistance and inflammatory markers [ 56 , 57 ] and HGS [ 15 ]. In addition, physical and sporting activities also control the accumulation of excess body fat, and there is an inverse association between accelerometer-measured physical activity and body fat mass in adolescents [ 58 ]. A study compared the HGS of children divided into two groups based on recommended physical activity guidelines (> 60 minutes of moderate and vigorous physical activity per day) and found that HGS was similar between the two groups [ 59 ]. Even if daily physical activity does not influence HGS in children and adolescents who meet the recommended criteria for physical activity guidelines [ 59 , 60 ], physical activity-induced changes in body fat mass may affect body mass and relative HGS per body mass. On the other hand, the type of sports or physical activity (gripping tools with the hands or not) may influence the improvement of HGS in developing children and adolescents [ 15 , 16 , 17 ]. However, it remains unclear how the type of sports or physical activity affects insulin resistance and inflammatory markers. Considering that differences in sport type (gripping tools with hands) influence the improvement of HGS and body composition in children and adolescents, investigating their effects on insulin resistance and inflammatory markers may help to understand the current study results. The quality and quantity of diets may be another factor influencing the association between absolute/relative HGS and insulin resistance and inflammatory markers. Poor dietary conditions may affect low HGS in children and adolescents [ 47 , 48 ]. A study reported the possible association of higher culinary preparation intake with higher HGS and that of high ultra-processed food with lower HGS in male teenagers [ 48 ]. Longitudinal follow-up studies reported a possibility that there is an association between the high consumption of ultra-processed foods and greater whole-body and abdominal adiposity [ 61 ]. Furthermore, it is known that insulin resistance and C-reactive protein are associated with excess body fat [ 62 , 63 ], and overweight and obesity are causes of low relative HGS. Studies included in this review reported that the proportion of children and adolescents who were overweight/obese was approximately 10–20% [ 31 , 32 , 34 , 37 ]. In contrast, no studies have controlled for the impact of body fat mass or percentage. Therefore, it remains possible that the influence of accumulated body fat on the reported results has yet to be excluded entirely. Lastly, the significance of the fact that the relative HGS divided by body mass, rather than the absolute value, was associated with insulin resistance and inflammatory markers is unclear. Therefore, it is not concluded from the meta-analysis results that high HGS is associated with desirable insulin resistance and inflammatory markers. The use of a ratio (HGS/body mass) can sometimes lead to spurious results [ 64 ]. This might explain the discrepancy between absolute and relative HGS. Many include relatives because they are trying to “control” the denominator. However, this assumes that the numerator is scaled the same across all levels of the denominator. In order to elucidate the relationship between the two, it may be necessary to investigate the association between changes in HGS and marker changes in insulin resistance and inflammation in children and adolescents rather than cross-sectional studies. Future research is needed to resolve this issue. Strength and Limitations To our knowledge, this is the first meta-analysis to provide an association between HGS and insulin resistance and inflammatory markers in children and adolescents. This review revealed that no longitudinal studies reported an association between the two. On the other hand, the cross-sectional studies used in the meta-analysis included studies with relatively large sample sizes. There are some limitations that need to be discussed. First, our meta-analysis results utilize results from cross-sectional studies, which limits the ability to determine the causality association between HGS and insulin resistance and inflammatory markers. Longitudinal follow-up research is needed to resolve these issues. Second, some studies have used BMI or pubertal status as surrogate markers of body fatness. Therefore, the results may differ when using the directly estimated body fat mass (or percentage) that impacts insulin resistance and inflammatory markers. Third, some included studies reported crude r values while others adjusted r values for sex and age and/or pubertal stage and body mass index, our results may be biased due confounding factors. Fourth, diet quality and balance may influence HGS and markers of insulin resistance inflammation, but almost no studies have controlled these effects. Fifth, most of the included studies in this review utilized HOMA-IR as an indicator of insulin resistance, with one study each utilizing QUICKI [ 37 ], which is a surrogate for glucose clamp-derived measures of insulin sensitivity, and oral glucose tolerance test (OGTT) [ 40 ]. One study [ 40 ] reported that the OGTT had a stronger relationship with relative HGS than the HOMA-IR, which may be helpful in future studies. Furthermore, it may be necessary to investigate the relationship with markers of protective effect against tissue inflammation. Sixth, some cross-sectional studies included in this systematic review were not included in the meta-analysis due insufficient information, and therefore, we strongly recommend future studies to also reporting r values for all correlation analyses. Seventh, due to the small number of studies included in each meta-analysis, we were unable to investigate the factors that could explain the significant statistical heterogeneity found for many analyses regarding relative HGS and insulin resistance and inflammatory markers. We were also unable to identify the presence or absence of publication bias due to the small number of studies included in each meta-analysis. Finally, the certainty of our estimates across primary outcomes was evaluated to be very low, which indicates a very uncertainty about the estimate. Thus, our results should be interpreted cautiously. Conclusion The results from cross-sectional studies indicated the association (very low evidence) between HGS and several markers of insulin resistance and inflammation existed when studies utilized the relative HGS per body mass. However, no significant relationship was found when studies used absolute HGS. The reason for the difference in the results between absolute and relative HGS is unknown, but lifestyle-related factors that affect HGS and insulin resistance and inflammatory markers may be involved, and these may not be adequately controlled. Furthermore, as longitudinal studies were limited and did not include enough results to conduct a meta-analysis, future longitudinal follow-up studies are an important means of resolving these issues. Abbreviations BMI body mass index C3 complement components 3 C4 complement components 4 CRP C-reactive protein GRADE certainty of evidence HGS handgrip strength HOMA-IR homeostatic model assessment for insulin resistance hs-CRP high-sensitive C-reactive protein IL-6 interleukin 6 OGTT oral glucose tolerance test QUICKI quantitative insulin sensitivity check index Declarations Supplemental Information Supplementary materials to this article can be found in the attached file. Authors’ contributions TA, RBV, AA, SM, HS, and JPL conceived and designed the study. TA and RBV conducted the literature search, data extraction, and preliminary analysis. TA and RBV drafted the initial manuscript and performed statistical analysis. AA, SM, HS, and JPL revised and edited the manuscript. All authors approved the final version. Funding This study was supported by a grant from the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (Grant Number: JP22K11610). Data availability All data generated or analyzed during this study are included in this published article and its supplementary information files. Ethics approval and consent to participate This study was based on previously conducted studies and did not involve any new experiments with human participants or animals performed by the authors. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Boonpor J, Parra-Soto S, Petermann-Rocha F, Ferrari G, Welsh P, Pell JP, et al. Associations between Grip Strength and Incident Type 2 Diabetes: Findings from the UK Biobank Prospective Cohort Study. BMJ Open Diabetes Res Care. 2021;9(1):e001865. https://doi.org/10.1136/bmjdrc-2020-001865 Li G, Qiao Y, Lu Y, Liu S, Chen X, Ke C. Role of Handgrip Strength in Predicting New-Onset Diabetes: Findings from the Survey of Health, Ageing and Retirement in Europe. BMC Geriatr. 2021;21(1):445. https://doi.org/10.1186/s12877-021-02382-9 Peralta M, Dias CM, Marques A, Henriques-Neto D, Sousa-Uva M. Longitudinal Association between Grip Strength and the Risk of Heart Diseases among European Middle-Aged and Older Adults. Exp Gerontol. 2023;171:112014. https://doi.org/10.1016/j.exger.2022.112014 Lopez-Bueno R, Andersen LL, Galatayud J, Casana J, Smith L, Jacob L, et al. Longitudinal association of handgrip strength with all-cause and cardiovascular mortality in older adults using a causal framework. Exp Gerontol. 2022;168:111951. https://doi.org/10.1016/j.exger.2022.111951 Parra-Soto S, Pell JP, Celis-Morales C, Ho FK. Absolute and Relative Grip Strength as Predictors of Cancer: Prospective Cohort Study of 445552 Participants in UK Biobank. J Cachexia Sarcopenia Muscle. 2022;13:325–32. https://doi.org/10.1002/jcsm.12863 Lopez-Bueno R, Andersen LL, Calatayud J, Casana J, Grabovac I, Oberndorfer M, et al. Association of Handgrip Strength with All-Cause and Cancer Mortality in Older Adults: A Prospective Cohort Study in 28 Countries. Age Ageing. 2022;51:afac117. https://doi.org/10.1093/ageing/afac117 Esteban-Cornejo I, Ho FK, Petermann-Rocha F, Lyall DM, Martinez-Gomez D, Cabanas-Sanchez V, et al. Handgrip Strength and All-Cause Dementia Incidence and Mortality: Findings from the UK Biobank Prospective Cohort Study. J Cachexia Sarcopenia Muscle. 2022;13:1514–25. https://doi.org/10.1002/jcsm.12857 Duchowny KA, Ackley SF, Brenowitz WD, Wang J, Zimmerman SC, Caunca MR, et al. Associations between Handgrip Strength and Dementia Risk, Cognition, and Neuroimaging Outcomes in the UK Biobank Cohort Study. JAMA Netw Open. 2022;5:e2218314. https://doi.org/10.1001/jamanetworkopen.2022.18314 McGrath R, Clark BC, Cesari M, Johnson C, Jurivich DA. Handgrip Strength Asymmetry Is Associated with Future Falls in Older Americans. Aging Clin Exp Res. 2021;33:2461–9. https://doi.org/10.1007/s40520-020-01757-z Willems SM, Wright DJ, Day FR, Trajanoska K, Joshi PK, Morris JA, et al. Large-scale GWAS identifies multiple loci for hand grip strength providing biological insights into muscular fitness. Nat Commun. 2017;8:16015. https://doi.org/10.1038/ncomms16015 Celis-Morales CA, Lyall DM, Anderson J, Iliodromiti S, Fan Y, Ntuk UE, et al. The association between physical activity and risk of mortality is modulated by grip strength and cardiorespiratory fitness: evidence from 498135 UK-Biobank participants. Eur Heart J. 2017;38:116–22. http://doi.org/10.1093/eurheart/ehw249 Bohannon RW. Grip Strength: An Indispensable Biomarker for Older Adults. Clin Interv Aging. 2019;14:1681–91. https://doi.org/10.2147/CIA.S194543 Abe T, Thiebaud SR, Ozaki H, Yamasaki S, Loenneke JP. Children with Low Handgrip Strength: A Narrative Review of Possible Exercise Strategies to Improve Its Development. Children. 2022;9(11):1616. https://doi.org/10.3390/children9111616 Buckner SL, Dankel SJ, Bell ZW, Abe T, Loenneke JP. The Association of Handgrip Strength and Mortality: What Does It Tell Us and What Can We Do with It? Rejuvenation Res. 2019;22(3):230–4. https://doi.org/10.1089/rej.2018.2111 Abe T, Abe A, Loenneke JP. Handgrip Strength of Young Athletes Differs Based on the Type of Sport Played and Age. Am J Hum Biol. 2024;36(5):e24022. https://doi.org/10.1002/ajhb.24022 Abe T, Kohmura Y, Suzuki K, Someya Y, Loenneke LP, Machida S, et al. Athletes in Sporting Events with Upper-Body Gripping Movements Have Greater Handgrip Strength than Those in Sporting Events That Prioritize the Lower Body. Am J Hum Biol. 2023;35(7):e23891. https://doi.org/10.1002/ajhb.23891 Abe T, Kohmura Y, Suzuki K, Someya Y, Loenneke JP, Machida S, et al. Handgrip Strength and Healthspan: Impact of Sports during the Developmental Period on Handgrip Strength (Juntendo Fitness plus Study). Juntendo Med J. 2023;69(5):400–4. https://doi.org/10.14789/jmj.JMJ23-0017-P Buckner SL, Dankel SJ, Mouser JG, Mattocks KT, Jessee MB, Loenneke JP. Chasing the top quartile of cross-sectional data: Is it possible with resistance training? Med Hypotheses. 2017;108:63–8. https://doi.org/10.1016/j.mehy.2017.08.009 Abe T, Viana RB, Dankel SJ, Loenneke JP. Different resistance exercise interventions for handgrip strength in apparently healthy adults: A systematic review. Int J Clin Med. 2023;14(12):552–81. https://doi.org/10.4236/ijcm.2023.1412047 Smith L, Yang L, Hamer M. Handgrip strength, inflammatory markers, and mortality. Scand J Med Sci Sports. 2019;29(8):1190–6. https://doi.org/10.1111/sms.13433 Tuttle CSL, Thang LAN, Maier AB. Markers of inflammation and their association with muscle strength and mass: A systematic review and meta-analysis. Ageing Res Rev. 2020;64:101185. http://doi.org/10.1016/j.arr.2020.101185 Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement. J Clin Epidemiol. 2021;134:103–12. doi: 10.1016/j.jclinepi.2021.02.003 Joanna Briggs Institute. Critical appraisal tools, 2020 . https://jbi.global/critical-appraisal-tools Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008;336(7650):924–6. https://doi.org/10.1136/bmj.39489.470347.AD Peterson RA, Brown SP. On the use of beta coefficients in meta-analysis. J Appl Psychol. 2005;90(1):175–81. https://doi.org/10.1037/0021-9010.90.1.175 Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to meta-analysis. In Introduction to Meta-Analysis . John Wiley and Sons, 2009. https://doi.org/10.1002/9780470743386 Munro BH. Statistical Methods for Health Care Research . JB Lippincott, 1986. Higgins J, Thomas J, Chandler J, Cumpston M, Li T, Page M, et al. Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023) (J. Chandler, M. Cumpston, T. Li, M. Page, & V. Welch (eds.)). Cochrane, 2023. www.training.cochrane.org/handbook JASP Team. JASP (Version 0.18.3)[Computer software], 2024. Agostinis-Sobrinho CA, Moreira C, Abreu S, Lopes L, Sardinha LB, Oliveira-Santos J, et al. Muscular fitness and metabolic and inflammatory biomarkers in adolescents: results from LabMed physical activity study. Scand J Med Sci Sports. 2017;27(12):1873–80. https://doi.org/10.1111/sms.12805 Artero EG, España-Romero V, Jiménez-Pavón D, Martinez-Gómez D, Warnberg J, Gómez-Martínez S, et al. Muscular fitness, fatness and inflammatory biomarkers in adolescents. Pediatr Obesity. 2014;9(5):391–400. https://doi.org/10.1111/j.2047-6310.2013.00186.x Artero EG, Ruiz JR, Ortega FB, España-Romero V, Vicente-Rodríguez G, Molnar D, et al. Muscular and cardiorespiratory fitness are independently associated with metabolic risk in adolescents: The HELENA study. Pediatr Diabetes. 2011;12(8):704–12. https://doi.org/10.1111/j.1399-5448.2011.00769.x Castro-Piñero J, Laurson KR, Artero EG, Ortega FB, Labayen I, Ruperez AI, et al. Muscle strength field-based tests to identify European adolescents at risk of metabolic syndrome: The HELENA study. J Sci Med Sport. 2019;22(8):929–34. https://doi.org/10.1016/j.jsams.2019.04.008 Cohen DD, Gómez-Arbeláez D, Camacho PA, Pinzon S, Hormiga C, Trejos-Suarez J, et al. Low muscle strength is associated with metabolic risk factors in Colombian children: The ACFIES study. PLoS ONE. 2014;9(4):1–10. https://doi.org/10.1371/journal.pone.0093150 Delgado-Alfonso A, Pérez-Bey A, Conde-Caveda J, Izquierdo-Gómez R, Esteban-Cornejo I, Gómez-Martínez S, et al. Independent and combined associations of physical fitness components with inflammatory biomarkers in children and adolescents. Pediatr Res. 2018;84(5):704–12. https://doi.org/10.1038/s41390-018-0150-5 Haapala EA, Kuronen E, Ihalainen JK, Lintu N, Leppänen MH, Tompuri T, et al. Cross-sectional associations between physical fitness and biomarkers of inflammation in children—The PANIC study. Scand J Med Sci Sports. 2023;33(6):1000–9. https://doi.org/10.1111/sms.14337 Jiménez-Pavón D, Ortega FB, Valtueña J, Castro-Piñero J, Gómez-Martínez S, Zaccaria M, et al. (2012). Muscular strength and markers of insulin resistance in European adolescents: The HELENA Study. Eur J Appl Physiol. 2012;112(7):2455–65. https://doi.org/10.1007/s00421-011-2216-5 Jung HW, Lee J, Kim J. Handgrip strength is associated with metabolic syndrome and insulin resistance in children and adolescents: analysis of Korea national health and nutrition examination survey 2014-2018. J Obes Metab Syndr. 2022;31(4):334–44. https://doi.org/10.7570/jomes22053 Lang JJ, Larouche R, Tremblay MS. The association between physical fitness and health in a nationally representative sample of Canadian children and youth aged 6 to 17 years. Health Promot Chronic Dis Prev Can. 2019;39(3):104–11. https://doi.org/10.24095/hpcdp.39.3.02 Li S, Zhang R, Pan G, Zheng L, Li C. Handgrip strength is associated with insulin resistance and glucose metabolism in adolescents: evidence from national health and nutrition examination survey 2011 to 2014. Pediatr Diabetes. 2018;19(3):375–80. https://doi.org/10.1111/pedi.12596 López-Gil JF, Weisstaub G, Ramírez-Vélez R, García-Hermoso A. Handgrip strength cut-off points for early detection of cardiometabolic risk in Chilean children. Eur J Pediatr. 2021;180(12):3483–9. https://doi.org/10.1007/s00431-021-04142-8 Tarp J, Bugge A, Møller NC, Klakk H, Rexen CT, Grøntved A, et al. Muscle fitness changes during childhood associates with improvements in cardiometabolic risk factors: a prospective study. J Phys Act Health. 2019;16(2):108–15. https://doi.org/10.1123/jpah.2017-0678 Zaqout M, Michels N, Bammann K, Ahrens W, Sprengeler O, Molnar D, et al. Influence of physical fitness on cardio-metabolic risk factors in European children. The IDEFICS study. Int J Obes. 2016;40(7):1119–25. https://doi.org/10.1038/ijo.2016.22 Demmer DL, Beilin LJ, Hands B, Burrows S, Cox KL, Straker LM, et al. Effects of muscle strength and endurance on blood pressure and related cardiometabolic risk factors from childhood to adolescence. J Hypertens. 2016;34(12):2365–75. https://doi.org/10.1097/HJH.0000000000001116 Abe T, Viana RB, Machida S, Loenneke JP. Impact of sports type on handgrip strength and morbidity/mortality. J Trainology. 2024;13(1):1–2. https://www.jstage.jst.go.jp/article/trainology/13/1/13_1/_article/-char/en Morales-Munoz I, Marwaha S, Upthegrove R, Cropley V. Role of inflammation in short sleep duration across childhood and psychosis in young adulthood. JAMA Psychiatry. 2024;81(8):825–83. https://doi.org/10.1001/jamapsychiatry.2024.0796 Bim MA, de Araujo Pinto A, Scarabelot KS, Claumann GS, Pelegrini A. Handgrip strength and associated factors among Brazilian adolescents: A cross-sectional study. J Bodyw Mov Ther. 2021;28:75–81. https://doi.org/10.1016/j.jbmt.2021.06.010 Confortin SC, Barbosa AR, de Oliveira BR, da Silva Magalhaes EI, Braganca MLBM, de Britto e Alves MTSS, et al. The consumption of culinary preparations and ultra-processed food is associated with handgrip strength in teenagers. Nutr J. 2022;21(1):66. https://doi.org/10.1186/s12937-022-00818-5 Bordoni A, Danesi F, Dardevet D, Dupont D, Fernandez AS, Gille D, et al. Dairy products and inflammation: A review of the clinical evidence. Crit Rev Food Sci Nutr. 2017;57(12):2497–525. https://doi.org/10.1080/10408398.2014.967385 Romero-Polvo A, Denova-Gutierrez E, Rivera-Paredez B, Castanon S, Gallegos-Carrillo K, Halley-Castillo E, et al. Association between dietary patterns and insulin resistance in Mexican children and adolescents. Ann Nutr Metab. 2012;61(2):142–50. https://doi.org/10.1159/000341493 Fraser BJ, Blizzard L, Buscot M J, Schmidt MD, Dwyer T, Venn AJ, et al. The association between grip strength measured in childhood, young- and mid-adulthood and prediabetes or type 2 diabetes in mid-adulthood. Sports Med. 2021;51(1):175–83. https://doi.org/10.1007/s40279-020-01328-2 Bohannon RW, Wang Y-C, Bubela D, Gershon RC. Handgrip strength: A population-based study of norms and age trajectories for 3- to 17-year-olds. Pediatr Phys Ther. 2017;29(2):118–23. https://doi.org/10.1097/PEP.0000000000000366 Abe A, Sanui R, Loenneke JP, Abe T. One-year handgrip strength change in kindergarteners depends upon physical activity status. Life (Basel). 2023;13(8):1665. https://doi.org/10.3390/life13081665 Sayer AA, Syddall HE, Gilbody HJ, Dennison EM, Cooper C. Does sarcopenia originate in early life? Finding from the Heartfordshire Cohort Study. J Gerontol A Biol Sci Med Sci. 2004;59(9):M930–4. https://10.1093/gerona/59.9.m930 Abe T, Abe A, Loenneke JP. Association of changes in grip strength with second digit length adjusted for fourth digit length in young children. Am J Hum Biol. 2023;35(8):e23901. https://doi.org/10.1002/ajhb.23901 Hardin DS, Azzarelli B, Edwards J, Wigglesworth J, Maianu L, Brechtel G, et al. Mechanisms of enhanced insulin sensitivity in endurance-trained athletes: Effects on blood flow and differential expression of GLUT 4 in skeletal muscles. J Clin Endocrinol Metab. 1995;80(8):2437–46. https://doi.org/10.1210/jcem.80.8.7629239 King DE, Carek P, Mainous III AG, Pearson WS. Inflammatory markers and exercise: Differences related to exercise type. Med Sci Sports Exerc. 2003;35(4):575–81. https://doi.org/10.1249/01.MSS.0000058440.28108.CC Basterfield L, Reilly JK, Pearce MS, Parkinson KN, Adamson AJ, Reilly JJ, et al. Longitudinal associations between sports participation, body composition and physical activity from childhood to adolescence. J Sci Med Sport. 2015;18(2):178–82. http://dx.doi.org/10.1016/j.jsams.2014.03.005 Tanaka C, Tremblay MS, Okuda M, Tanaka S. Association between 24-hour movement guidelines and physical fitness in children. Pediatr Int. 2020;62(12):1381–7. https://doi.org/10.1111/ped.14322 Tapia-Serrano MA, Lopez-Gil JF, Sevil-Serrano J, Garcia-Hermoso A, Sanchez-Miguel PA. What is the role of adherence to 24-hour movement guidelines in relation to physical fitness components among adolescents? Scand J Med Sci Sports. 2023;33(8):1373–83. https://doi.org/10.1111/sms.14357 De Amicis R, Mambrini SP, Pellizzari M, Foppiani A, Bertoli S, Battezzati A, et al. Ultra-processed foods and obesity and adiposity parameters among children and adolescents: a systematic review. Eur J Nutr. 2022;61(5):2297–311. https://doi.org/10.1007/s00394-022-02873-4 Maffeis C, Morandi A. Body composition and insulin resistance in children. Eur J Clin Nutr. 2018;72(9):1239–45. https://doi.org/10.1038/s41430-018-0239-2 Navarro P, de Dios O, Gavela-Perez T, Jois A, Garcea C, Soriano-Guillen L. High-sensitivity C-reactive protein and leptin levels related to body mass index changes throughout childhood. J Pediatr. 2016;178:178–82. https://doi.org/10.1016/j.jpeds.2016.08.020 Curran-Everett D. Explorations in statistics: the analysis of ratios and normalized data. Adv Physiol Educ. 2013;37(3):213–9. https://doi.org/10.1152/advan.00053.2013 Tables Tables 1 to 3 are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryInformationIJP.docx Tables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6291913","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":472948510,"identity":"dc8077c6-a8a6-4cd6-bcf3-96cbaca865a8","order_by":0,"name":"Takashi Abe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYDACZuYDzAwMNkAWY+MBonTwsLMlALWkgbQ0EKmFn8cAqOUwmEOcFntmBsPPBRXn7da2HwbaUmMTTdgWZoZk6RlnbidvO5MI1HIsLbeBCC0HpHnbbiebHQBqYWw4TIwWxubfvP/OJZudf0i0FmY2ad6GA3ZmN4i25TAbm/WMY8kJZjeAtiQQ4xf2/vOfbxfU2NmbnU9/+OBDjQ1hLTCQCFaZQKxyELAnRfEoGAWjYBSMMAAALCtCV5IHpBYAAAAASUVORK5CYII=","orcid":"","institution":"Graduate School of Health and Sports Science, Institute of Health and Sports Science \u0026 Medicine, Juntendo University","correspondingAuthor":true,"prefix":"","firstName":"Takashi","middleName":"","lastName":"Abe","suffix":""},{"id":472948511,"identity":"46d883a2-86e4-4537-924f-e0b8d1a6a7e3","order_by":1,"name":"Ricardo B. Viana","email":"","orcid":"","institution":"Institute of Physical Education and Sport, Federal University of Ceará, Fortaleza","correspondingAuthor":false,"prefix":"","firstName":"Ricardo","middleName":"B.","lastName":"Viana","suffix":""},{"id":472948512,"identity":"514ea8ae-9c1d-4493-8937-c4bd2e83e1ef","order_by":2,"name":"Akemi Abe","email":"","orcid":"","institution":"Division of Children’s Health and Exercise Research, Institute of Trainology","correspondingAuthor":false,"prefix":"","firstName":"Akemi","middleName":"","lastName":"Abe","suffix":""},{"id":472948513,"identity":"506f7333-296f-447a-9bfd-9ffefac7b482","order_by":3,"name":"Shuichi Machida","email":"","orcid":"","institution":"Graduate School of Health and Sports Science, Institute of Health and Sports Science \u0026 Medicine, Juntendo University","correspondingAuthor":false,"prefix":"","firstName":"Shuichi","middleName":"","lastName":"Machida","suffix":""},{"id":472948514,"identity":"d208a1c3-cee6-490c-8ce5-9ccb8075a835","order_by":4,"name":"Hisashi Naito","email":"","orcid":"","institution":"Graduate School of Health and Sports Science, Institute of Health and Sports Science \u0026 Medicine, Juntendo University","correspondingAuthor":false,"prefix":"","firstName":"Hisashi","middleName":"","lastName":"Naito","suffix":""},{"id":472948515,"identity":"68dcb992-cb73-4337-9a08-a44f169c45ec","order_by":5,"name":"Jeremy P. Loenneke","email":"","orcid":"","institution":"Department of Health, Exercise Science, and Recreation Management, The University of Mississippi","correspondingAuthor":false,"prefix":"","firstName":"Jeremy","middleName":"P.","lastName":"Loenneke","suffix":""}],"badges":[],"createdAt":"2025-03-24 05:42:12","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6291913/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6291913/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85184666,"identity":"f00c5cef-13f0-47bb-9349-2ec7fee6fcf5","added_by":"auto","created_at":"2025-06-23 07:57:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":262936,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flow diagram of the selection process for the studies included in this systematic review and meta-analysis. n indicates the number of studies. CENTRAL: Cochrane Central Register of Controlled Trials. Embase: Excerpta Medica Database. Medline/Pubmed: Medical Literature Analysis and Retrieval System Online. HGS: handgrip strength.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6291913/v1/de6114be26bbfb68cb899de7.png"},{"id":85183469,"identity":"7ca224f0-da47-4222-ad9c-2bdfcf9419f7","added_by":"auto","created_at":"2025-06-23 07:49:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":271421,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between HGS and insulin resistance markers.The black box represents study’s effect size, and the box size reflects study’s relative weight. The continuous black line represents the study’s 95% CI. The black diamond represents the aggregate effect size and 95% CI. Fisher’s Z: Fisher’s r-to-z transformed correlation coefficients. CI: confidence interval. HGS: handgrip strength. HOMA-IR: model assessment-estimated insulin resistance. REML: Restricted maximum likelihood estimation.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6291913/v1/d99c0777ebb95eb159d3ce5b.png"},{"id":85183473,"identity":"a8e81a3e-2d82-4154-93ad-5c95cb3854b6","added_by":"auto","created_at":"2025-06-23 07:49:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":285673,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between relative HGS and insulin resistance markers. The black box represents study’s effect size, and the box size reflects study’s relative weight. The continuous black line represents the study’s 95% CI. The black diamond represents the aggregate effect size and 95% CI. Fisher’s Z: Fisher’s r-to-z transformed correlation coefficients. CI: confidence interval. HGS/BW: handgrip strength relativized by body mass. HOMA-IR: model assessment-estimated insulin resistance. REML: Restricted maximum likelihood estimation.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6291913/v1/8b716ccf916808a99b462c58.png"},{"id":85183475,"identity":"eea4b65e-defa-4aee-b9a1-6f321febf3c5","added_by":"auto","created_at":"2025-06-23 07:49:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":209587,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between relative HGS and inflammatory markers. The black box represents study’s effect size, and the box size reflects study’s relative weight. The continuous black line represents the study’s 95% CI. The black diamond represents the aggregate effect size and 95% CI. Fisher’s Z: Fisher’s r-to-z transformed correlation coefficients. CI: confidence interval. HGS/BW: handgrip strength relativized by body mass. CRP: C-reactive protein. hs-CRP: high-sensitivity C-reactive protein. C3: complement component 3. C4: complement component 4. IL-6: interleukin-6. REML: Restricted maximum likelihood estimation.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6291913/v1/be4f0e60a4bca03c3daa7a60.png"},{"id":85185103,"identity":"9305e4d8-6ed3-4c7d-bec1-5fb9ffa9ffe9","added_by":"auto","created_at":"2025-06-23 08:05:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2096870,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6291913/v1/d0700749-0451-4077-a1a8-865105febcab.pdf"},{"id":85183472,"identity":"aa668fd6-e082-47db-8396-308d76d198fa","added_by":"auto","created_at":"2025-06-23 07:49:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":371763,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationIJP.docx","url":"https://assets-eu.researchsquare.com/files/rs-6291913/v1/e49fde980266a4ad527bf178.docx"},{"id":85184667,"identity":"96229470-07b7-4742-b3f1-10b729055897","added_by":"auto","created_at":"2025-06-23 07:57:03","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":51019,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6291913/v1/da14ec25ae39aad5c466576a.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAssociations between handgrip strength and markers of insulin resistance and inflammation in childhood and adolescence: A systematic review with meta-analysis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLarge-scale longitudinal studies in middle-aged and older adults have repeatedly reported inverse associations between handgrip strength (HGS) and the risk of diabetes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], heart disease [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], cancer [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], dementia [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and falls [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These associations remain even when adjusting for age, education level, body mass index, alcohol, tobacco, medical history, and others. Genetic [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and nongenetic [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] factors have been proposed to explain these associations. However, what mechanisms explain the inverse association between HGS and morbidity/mortality remains unclear.\u003c/p\u003e \u003cp\u003eAlthough HGS is a biomarker [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], whether it can improve morbidity and mortality when increased by environmental factors such as sports and exercise training has also yet to be demonstrated [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Our recent studies revealed that HGS may increase through select sports (i.e., whether or not an athlete plays with sports equipment in their hands) during the period of development [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and it is possible to affect HGS in young adulthood [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]). The importance of HGS levels acquired during the developmental period is understandable, given that HGS, determined in early adulthood, changes significantly only when age-related decline or injury/disease occurs [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, the HGS acquired during development may be associated with protection or resistance to developing lifestyle-related disease risk factors, influencing morbidity and mortality throughout life.\u003c/p\u003e \u003cp\u003eA follow-up study reported inverse associations between baseline HGS and changes in inflammation markers in older women and speculated that inflammation markers partly explained the association between HGS and mortality [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A systematic review and meta-analysis recently reported that higher levels of circulating inflammatory markers are significantly associated with lower muscle strength, including HGS in adults (\u0026ge;\u0026thinsp;18 years) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, whether similar associations between HGS and risk factors of health-related diseases exist in children and adolescents is unclear. Thus, this systematic review with meta-analysis investigated the cross-sectional and longitudinal associations between HGS and markers of insulin resistance and inflammation in children and adolescents using baseline values and/or change scores. Our hypotheses are that (i) there would be significant cross-sectional associations between the HGS and insulin resistance and inflammatory markers, and (ii) there would be significant longitudinal associations between changes in HGS and changes in insulin resistance and inflammatory markers.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe performed this systematic review according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. The study was pre-registered (February 3, 2024) in the International Prospective Register of Systematic Review (PROSPERO) (CRD42024502179).\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eSearch strategy\u003c/h2\u003e\n \u003cp\u003eEnglish-language searches of the electronic databases Medical Literature Analysis and Retrieval System Online (MEDLINE/PubMed), Scopus, Web of Science, Excerpta Medica Database (Embase), and Cochrane Central Register of Controlled Trials (CENTRAL) were run by two independent researchers (T.A. and R.V). Articles were retrieved from electronic databases combining the following terms: (handgrip strength OR grip strength OR grip) AND (resistin or insulin resistance or insulin sensitivity or inflammation or cytokines OR acute phase proteins) AND (child or children or ten or teenager or pediatric or adolescents or adolescence or juvenile). Supplementary Material 1 shows the completed search strategy.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eEligibility criteria\u003c/h3\u003e\n\u003cp\u003eObservational studies examining the association between insulin resistance or inflammatory markers and HGS as primary or secondary aim through any type of measurement and collected during childhood age (\u0026lt;\u0026thinsp;18 years) were included. Studies were excluded based on the following file types: study protocols, conference papers, letters to the editor, books, book sections, theses, film/broadcasts, case studies/reports, opinion articles, abstracts, or reviews. Rayyan software was used independently by two researchers (T.A. and R.V.) to remove duplicates and apply the eligibility criteria with disagreements resolved by a consensus between both researchers.\u003c/p\u003e\n\u003ch3\u003eData extraction\u003c/h3\u003e\n\u003cp\u003eThe following study characteristics were extracted: authors, publication year, country, study design (cross-sectional or longitudinal [cohort or case-control]), participant characteristics (age, body mass, height, body mass index, sex, and study sample), sample size, insulin resistance markers, inflammatory markers, HGS, effect size, technique used to measure insulin resistance, inflammation, and HGS, and information pertaining to methodological quality. In the event that the same participants are included across multiple articles, the study with the largest sample size and most comprehensive data extraction information was selected. If the same participants are included across multiple articles but the available data are from different outcomes [e.g., fasting glucose, fasting insulin, homeostatic model assessment for insulin resistance (HOMA-IR), and quantitative insulin sensitivity check index (QUICKI)] both effect sizes were extracted and examined in separate meta-analyses. If a study analyzed the relationship between HGS and more than one insulin resistance marker or inflammatory marker concurrently, effect sizes for each association were calculated. These data were extracted independently by two researchers (T.A. and R.V.) with disagreements resolved by a consensus between both researchers.\u003c/p\u003e\n\u003ch3\u003eStudy quality assessment\u003c/h3\u003e\n\u003cp\u003eMethodological quality of the included studies was assessed using the Joanna Briggs Critical Appraisal Tool [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. Studies were assessed on an 8-point (cross-sectional) or 10-point (case-control) or 11-point (cohort) scale. Each criterion was coded as \u0026ldquo;Yes (1), \u0026ldquo;No\u0026rdquo; (0), \u0026ldquo;Unclear\u0026rdquo; (0) or \u0026ldquo;Not Applicable\u0026rdquo; and a proportion of total quality assessment score for each study was calculated based on the total number of items applicable to the study. Studies with a score higher than 70% were classified as having a high quality, those with a score between 50% and 70% as having a medium quality, and those with a score less than 50% as having a low quality. Two researchers (R.V. and T.A.) assessed the methodological quality.\u003c/p\u003e\n\u003ch3\u003eCertainty of evidence assessment\u003c/h3\u003e\n\u003cp\u003eBased on the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) method, one author (R.V.) rated the certainty for the main comparison and outcome as very low (very uncertainty about the estimate), low (research is very likely to significantly affect our confidence in estimating the effect and is likely to change the estimate), moderate (further research is likely to have an important impact on our confidence in estimating the effect and may change the estimate), or high (further research is very unlikely to change our confidence in estimating the effect) [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eFor the cross-sectional studies that only reported standardized beta (\u003cem\u003e\u0026beta;)\u003c/em\u003e coefficients within the range from \u0026minus;\u0026thinsp;0.50 to 0.50, correlation coefficient (\u003cem\u003er)\u003c/em\u003e values were calculated by the following equation: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.98\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;+\u0026thinsp;0.05\u0026lambda; [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]; where \u0026lambda; is an indicator variable that equals 1 when \u0026lambda; is nonnegative and 0 when \u0026lambda; is negative [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. After obtaining \u003cem\u003er\u003c/em\u003e values, Fisher\u0026rsquo;s Z values for each cross-sectional study were calculated from \u003cem\u003er\u003c/em\u003e values by the following equation:\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003cimg src=\"data:image/png;base64,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\" height=\"40\" width=\"149\"\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]; where ln(x) is the natural logarithm function. Standard error of Fisher\u0026rsquo;s Z values was calculated the following equation:\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003cimg src=\"data:image/png;base64,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\" width=\"95\" height=\"61\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. For the cross-sectional studies that reported \u003cem\u003er\u003c/em\u003e values split by sex (boys and girls) or by age (children and youth), it was converted to Fisher\u0026rsquo;s Z values and combined to a single value (effect), as recommend by Borenstein et al. [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. Correlation meta-analyses using Fisher\u0026rsquo;s r-to-z transformed correlation coefficients were performed to determine the overall correlation between HGS (absolute and relativized by body mass) and insulin resistance and inflammatory markers. For that, Fisher\u0026rsquo;s Z, and its respective standard error values were pooled under a random effect model. The results, such as the summary effect and its confidence interval, were then converted back to \u003cem\u003er\u003c/em\u003e values for presentation [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. The \u003cem\u003er\u003c/em\u003e values were interpreted per Pearson thresholds [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]: trivial (\u0026lt;\u0026thinsp;0.10), small (0.10 to \u0026lt;\u0026thinsp;0.30), moderate (0.30 to \u0026lt;\u0026thinsp;0.50), and large (\u0026ge;\u0026thinsp;0.5). Random effects model was used to reduce the risk of unknown factors responsible for variability even under homogeneity. Restricted maximum likelihood estimation was used in all models. To improve our results, we conducted several sensitivity analyses (the one study removed method) to consider the influence of each study on the overall results. Due to insufficient longitudinal included studies and available information, it was not possible to pooled data for associations between changes in HGS and the changes in insulin resistance and inflammatory markers.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eStatistical heterogeneity was assessed using \u0026tau;\u003csup\u003e2\u003c/sup\u003e, H\u003csup\u003e2\u003c/sup\u003e, Q statistic, and the inconsistency I\u003csup\u003e2\u003c/sup\u003e test. The I\u003csup\u003e2\u003c/sup\u003e statistic estimates the percentage variance between studies and can be roughly interpreted as low (0\u0026ndash;40%), moderate (30\u0026ndash;60%), substantial (50\u0026ndash;90%), or considerable (75\u0026ndash;100%) heterogeneity. To note, the I\u003csup\u003e2\u003c/sup\u003e classifications overlap as these are rough guidelines suggested by Higgins et al. [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. Publication bias was visually assessed using funnel plots by plotting the effect size of each trial against its standard error. As recommended by Higgins et al. [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e], \u0026ldquo;Egger\u0026rsquo;s regression test\u0026rdquo; was not performed to assess asymmetry of the funnel plot because all between-groups meta-analyses involved less than 10 original studies. All statistical analyses were performed in the Jeffreys\u0026rsquo;s Amazing Statistics Program (JASP, 0.18.3.0, Netherlands) using an alpha level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. All statistical analyses were be performed using an alpha level of p\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eIncluded studies\u003c/h2\u003e\n \u003cp\u003eThe search strategy retrieved 1,811 records (Embase [\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,057], CENTRAL [\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;45], MEDLINE [\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;260], Scopus [\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;274], and Web of Science [\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;175]). After duplications were removed (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;239), title and abstract of 1,572 records were screened and 1,553 records were eliminated due the following reasons: wrong population (n\u0026thinsp;=\u0026thinsp;1118), wrong outcome (n\u0026thinsp;=\u0026thinsp;181), review (n\u0026thinsp;=\u0026thinsp;154), case report (n\u0026thinsp;=\u0026thinsp;30), book (n\u0026thinsp;=\u0026thinsp;28), wrong study, design (n\u0026thinsp;=\u0026thinsp;21), conference proceedings (n\u0026thinsp;=\u0026thinsp;10), study protocol (n\u0026thinsp;=\u0026thinsp;8), book chapter (n\u0026thinsp;=\u0026thinsp;2), and letter to editor (n\u0026thinsp;=\u0026thinsp;1). The remaining 19 full-text articles were reviewed further, with four studies excluded due the following reasons: wrong population (n\u0026thinsp;=\u0026thinsp;3), and no correlation data available for HGS and insulin resistance or inflammatory markers (n\u0026thinsp;=\u0026thinsp;1) (Supplementary material 2). Thus, 15 studies were included in this systematic review (12 cross-sectional [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e], two cross-sectional and longitudinal [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e], and one longitudinal [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]. As eight [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e] of the 14 cross-sectional included studies did not report \u003cem\u003er\u003c/em\u003e values for the cross-sectional correlation between HGS and inflammatory and/or insulin resistance markers, this information was requested for the correspondence authors. However, only four correspondence authors sent the data requested [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e], one author answered that raw data was lost and informed that B coefficients were unstandardized [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e], one author decline our request [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e] and the remaining two authors did not respond our request [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]. Thus, the \u003cem\u003er\u003c/em\u003e values for one [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e] of the two studies [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e] that reported standardized \u003cem\u003e\u0026beta;\u003c/em\u003e coefficients within the range from \u0026minus;\u0026thinsp;0.50 to 0.50 for the cross-sectional analyses were estimated as previously reported in the statistical analysis section. Three studies [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e] were excluded from the meta-analysis because it was not possible to estimate \u003cem\u003er\u003c/em\u003e values and one study [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e] was excluded because HGS was relativized by lean mass [Supplementary material 2]. Therefore, 11 of the 15 included studies were also included in the meta-analysis for cross-sectional correlation (eight for insulin resistance [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e] and four for inflammation [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e] [Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e]. The included studies were published from 2011 [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e] up to 2023 [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e] [Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eParticipant characteristics\u003c/h2\u003e\n \u003cp\u003eParticipants\u0026rsquo; characteristics are summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Almost one-third of the included studies (n\u0026thinsp;=\u0026thinsp;6) were conducted with adolescents [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e], while the other four studies were conducted with children [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e], and five were mixed children and adolescents [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]. The age range of the samples in many studies was between five and eight years [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e], although the range was narrower for studies of children [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e] and wider for mixed children and adolescents [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]. The study sample was collected from Europe [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e], North America [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e], South America [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e], Asian [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e] countries, and Australia [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]. Five studies reported the prevalence of overweight and obesity [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e], while other studies did not clearly describe them.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eHGS assessment\u003c/h2\u003e\n \u003cp\u003eElectronic digital or analog hand dynamometers were used for measuring HGS in 11 studies for meta-analysis [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]. Almost half of these studies (n\u0026thinsp;=\u0026thinsp;6) did not clearly report which posture (e.g., standing or sitting) was adopted for HGS measurements [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e], but five other studies performed measurements in the standing position [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]. In almost all studies included in the meta-analyses [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e], the grip span was adjusted to participants\u0026apos; hand size.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eMeta-analyses: HGS and insulin resistance markers\u003c/h2\u003e\n \u003cp\u003eThe available data from the included studies [Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e] allowed us to conduct correlation meta-analyses pooling Fisher\u0026rsquo;s r-to-z transformed correlation coefficients for absolute HGS and fasting glucose, fasting insulin, and HOMA-IR, as well as for relative HGS (relativized by body mass) and these same insulin resistance markers. All meta-analyses pooling Fisher\u0026rsquo;s r-to-z transformed correlation coefficients are summarized in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eFasting glucose\u003c/h2\u003e\n \u003cp\u003eFour studies [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e] with four independent comparisons provided data from 5252 children and youth individuals to analyze the relationship between absolute HGS and fasting glucose. There was no significant correlation (k\u0026thinsp;=\u0026thinsp;4; c\u0026thinsp;=\u0026thinsp;4; n\u0026thinsp;=\u0026thinsp;5252; Fisher\u0026rsquo;s z\u0026thinsp;=\u0026thinsp;0.004; 95% CI = \u0026minus;\u0026thinsp;0.06 to 0.07, p\u0026thinsp;=\u0026thinsp;0.902) between absolute HGS and fasting glucose [Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA] with considerable heterogeneity (\u0026tau;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.004, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;80.6%, H\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;5.2, Q [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]\u0026thinsp;=\u0026thinsp;13.951, p\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e\n \u003cp\u003eFour studies [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e] with four independent comparisons provided data from 4971 children and youth individuals to analyze the relationship between relative HGS and fasting glucose. There was a significant negative correlation (k\u0026thinsp;=\u0026thinsp;4; c\u0026thinsp;=\u0026thinsp;4; n\u0026thinsp;=\u0026thinsp;4971; Fisher\u0026rsquo;s z = \u0026minus;\u0026thinsp;0.06; 95% CI = \u0026minus;\u0026thinsp;0.09 to \u0026minus;\u0026thinsp;0.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between relative HGS and fasting glucose [Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA] with low heterogeneity (\u0026tau;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.000004, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.4%, H\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.0, Q [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]\u0026thinsp;=\u0026thinsp;4.422, p\u0026thinsp;=\u0026thinsp;0.219).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eFasting insulin\u003c/h2\u003e\n \u003cp\u003eThree studies [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e] with three independent comparisons provided data from 4302 children and youth individuals to analyze the relationship between absolute HGS and fasting insulin. There was no significant correlation (k\u0026thinsp;=\u0026thinsp;3; c\u0026thinsp;=\u0026thinsp;3; n\u0026thinsp;=\u0026thinsp;4302; Fisher\u0026rsquo;s z\u0026thinsp;=\u0026thinsp;0.0008; 95% CI = \u0026minus;\u0026thinsp;0.03 to 0.03, p\u0026thinsp;=\u0026thinsp;0.962) between absolute HGS and fasting glucose [Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB] with low heterogeneity (\u0026tau;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0001, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;13.0%, H\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.2, Q [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]\u0026thinsp;=\u0026thinsp;1.738, p\u0026thinsp;=\u0026thinsp;0.419).\u003c/p\u003e\n \u003cp\u003eFour studies [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e] with four independent comparisons provided data from 5252 children and youth individuals to analyze the relationship between relative HGS and fasting insulin. There was a significant negative correlation (k\u0026thinsp;=\u0026thinsp;4; c\u0026thinsp;=\u0026thinsp;4; n\u0026thinsp;=\u0026thinsp;5252; Fisher\u0026rsquo;s z = \u0026minus;\u0026thinsp;0.23; 95% CI = \u0026minus;\u0026thinsp;0.31 to \u0026minus;\u0026thinsp;0.14, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between relative HGS and fasting insulin [Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB] with considerable heterogeneity (\u0026tau;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.006, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;87.8%, H\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;8.2, Q [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]\u0026thinsp;=\u0026thinsp;30.706, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eHOMA-IR\u003c/h2\u003e\n \u003cp\u003eFive [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e] studies with five independent comparisons provided data from 6370 children and youth individuals to analyze the relationship between absolute HGS and HOMA-IR. There was no significant correlation (k\u0026thinsp;=\u0026thinsp;5; c\u0026thinsp;=\u0026thinsp;5; n\u0026thinsp;=\u0026thinsp;6370; Fisher\u0026rsquo;s z\u0026thinsp;=\u0026thinsp;0.09; 95% CI = \u0026minus;\u0026thinsp;0.04 to 0.22, p\u0026thinsp;=\u0026thinsp;0.190) between absolute HGS and HOMA-IR [Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC] with considerable heterogeneity (\u0026tau;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.021, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;96.0%, H\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;25.1, Q [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]\u0026thinsp;=\u0026thinsp;65.144, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cp\u003eSix studies [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e] with six independent comparisons provided data from 6192 children and youth individuals to analyze the relationship between relative HGS and HOMA-IR. There was a significant negative correlation (k\u0026thinsp;=\u0026thinsp;6; c\u0026thinsp;=\u0026thinsp;6; n\u0026thinsp;=\u0026thinsp;6192; Fisher\u0026rsquo;s z = \u0026minus;\u0026thinsp;0.22; 95% CI = \u0026minus;\u0026thinsp;0.28 to \u0026minus;\u0026thinsp;0.15, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between relative HGS and HOMA-IR [Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC] with considerable heterogeneity (\u0026tau;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.004, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;80.0%, H\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;5.0, Q [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]\u0026thinsp;=\u0026thinsp;34.849, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eMeta-analyses: HGS and inflammatory markers\u003c/h2\u003e\n \u003cp\u003eThe available data from the included studies [Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e] allowed us to conduct correlation meta-analyses polling Fisher\u0026rsquo;s r-to-z transformed correlation coefficients for relative HGS (relativized by body mass) and C-reactive protein (CRP), high-sensitive C-reactive protein (hs-CRP), complement components 3 (C3) and 4 (C4), and interleukin-6 (IL-6) [Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eCRP and hs-CRP\u003c/h2\u003e\n \u003cp\u003eTwo studies [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e] with two independent comparisons provided data from 1142 children and youth individuals to analyze the relationship between relative HGS and CRP. There was a significant negative correlation (k\u0026thinsp;=\u0026thinsp;2; c\u0026thinsp;=\u0026thinsp;2; n\u0026thinsp;=\u0026thinsp;1142; Fisher\u0026rsquo;s z = \u0026minus;\u0026thinsp;0.18; 95% CI = \u0026minus;\u0026thinsp;0.26 to \u0026minus;\u0026thinsp;0.10, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between relative HGS and CRP (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA) with moderate heterogeneity (\u0026tau;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.001, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;44.7%, H\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.8, Q [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]\u0026thinsp;=\u0026thinsp;1.810, p\u0026thinsp;=\u0026thinsp;0.179).\u003c/p\u003e\n \u003cp\u003eTwo studies [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e] with two independent comparisons provided data from 1198 children and youth individuals to analyze the relationship between relative HGS and hs-CRP. There was a significant negative correlation (k\u0026thinsp;=\u0026thinsp;2; c\u0026thinsp;=\u0026thinsp;2; n\u0026thinsp;=\u0026thinsp;1198; Fisher\u0026rsquo;s z = \u0026minus;\u0026thinsp;0.18; 95% CI = \u0026minus;\u0026thinsp;0.31 to \u0026minus;\u0026thinsp;0.04, p\u0026thinsp;=\u0026thinsp;0.013) between relative HGS and hs-CRP [Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB] with considerable heterogeneity (\u0026tau;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.008, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;86.6%, H\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;5.7, Q [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]\u0026thinsp;=\u0026thinsp;5.751, p\u0026thinsp;=\u0026thinsp;0.016).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eComplement components 3 (C3) and 4 (C4)\u003c/h2\u003e\n \u003cp\u003eThree studies [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e] with three independent comparisons provided data from 1671 children and youth individuals to analyze the relationship between relative HGS and C3 and C4. There was a significant negative correlation (k\u0026thinsp;=\u0026thinsp;3; c\u0026thinsp;=\u0026thinsp;3; n\u0026thinsp;=\u0026thinsp;1671; Fisher\u0026rsquo;s z = \u0026minus;\u0026thinsp;0.23; 95% CI = \u0026minus;\u0026thinsp;0.28 to \u0026minus;\u0026thinsp;0.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between relative HGS and C3 [Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC] with low heterogeneity (\u0026tau;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.000, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0%, H\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.0, Q [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]\u0026thinsp;=\u0026thinsp;1.613, p\u0026thinsp;=\u0026thinsp;0.446). There was also a significant negative correlation (k\u0026thinsp;=\u0026thinsp;3; c\u0026thinsp;=\u0026thinsp;3; n\u0026thinsp;=\u0026thinsp;1671; Fisher\u0026rsquo;s z = \u0026minus;\u0026thinsp;0.17; 95% CI = \u0026minus;\u0026thinsp;0.22 to \u0026minus;\u0026thinsp;0.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between relative HGS and C4 [Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD] with low heterogeneity (\u0026tau;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.000, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0%, H\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.0, Q [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]\u0026thinsp;=\u0026thinsp;0.920, p\u0026thinsp;=\u0026thinsp;0.631).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eIL-6\u003c/h2\u003e\n \u003cp\u003eTwo studies [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e] with two independent comparisons provided data from 1032 children and youth individuals to analyze the relationship between relative HGS and IL-6. There was no significant correlation (k\u0026thinsp;=\u0026thinsp;2; c\u0026thinsp;=\u0026thinsp;2; n\u0026thinsp;=\u0026thinsp;1032; Fisher\u0026rsquo;s z = \u0026minus;\u0026thinsp;0.08; 95% CI = \u0026minus;\u0026thinsp;0.26 to 0.11, p\u0026thinsp;=\u0026thinsp;0.413) between relative HGS and IL-6 [Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE] with considerable heterogeneity (\u0026tau;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.016, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;89.0%, H\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;9.1, Q [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]\u0026thinsp;=\u0026thinsp;9.114, p\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eSensitivity analyses\u003c/h2\u003e\n \u003cp\u003eA sensitivity analysis (the one study removed method) revealed that significant negative correlations between relative HGS and fasting insulin (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), HOMA-IR (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), C3 (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and C4 (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) remained after removing each one of the studies included in the main meta-analyses [Supplementary Material 3]. In addition, a significant negative correlation between relative HGS and fasting glucose remained after removing Cohen et al. [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e], Jim\u0026eacute;nez-Pav\u0026oacute;n et al. [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e], and L\u0026oacute;pez-Gil et al.\u0026rsquo;s [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e] studies but not after removing Jung et al. [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e] study [Supplementary Material 3]. Sensitive analyses were not performed for correlations between relative HGS and h-CRP or CRP because only two studies were included in their respective main meta-analyses.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003eSubgroup analyses\u003c/h2\u003e\n \u003cp\u003eDue to the small number of included studies (k\u0026thinsp;\u0026lt;\u0026thinsp;10), preplanned subgroup analyses were not performed to test whether the study design (cross-sectional and longitudinal studies) would influence the results.\u003c/p\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003ePublication bias\u003c/h2\u003e\n \u003cp\u003eDue to the small number of included studies, a visual analysis of the effect sizes (i.e., Fisher\u0026rsquo;s r-to-z transformed correlation coefficients) of the test results for resistance insulin and inflammatory markers did not indicate the presence or absence of publication bias [Supplementary Material 4]. As reported in the statistical analysis section, \u0026ldquo;Egger\u0026rsquo;s regression test\u0026rdquo; was not performed to assess the asymmetry of the funnel plot because the meta-analyses involved less than 10 original studies [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy quality\u003c/h2\u003e\n \u003cp\u003eAll the cross-sectional included studies (n\u0026thinsp;=\u0026thinsp;12) [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e] presented a good quality (\u0026gt;\u0026thinsp;75%) in the Joanna Briggs Critical Appraisal Tool. Most of the cross-sectional included studies (66.7%, n\u0026thinsp;=\u0026thinsp;8 of 12) [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e] reached 7 points (87.5%) in the Joanna Briggs Critical Appraisal Tool, while a quarter of the cross-sectional included studies (n\u0026thinsp;=\u0026thinsp;3 of 12) [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e] reached the highest score possible (8 points, which correspond to 100%) [Supplementary Material 5]. All longitudinal included studies (n\u0026thinsp;=\u0026thinsp;3) [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e] reached the six points in the Joanna Briggs Critical Appraisal Tool, which correspond to 66.7% (moderate quality) of the highest possible score (9 points) [Supplementary Material 6].\u003c/p\u003e\n \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n \u003ch2\u003eCertainty of evidence (GRADE)\u003c/h2\u003e\n \u003cp\u003eBecause the present meta-analysis included only cross-sectional studies, the quality (level) rating of the evidence using GRADE started as low for all primary \u0026ldquo;outcomes\u0026rdquo; [Supplementary Material 7]. The evidence was not downgraded by one level for risk of bias (study quality) because all included studies presented a good study quality, as well as the evidence was not downgraded by one level for indirectness because the included studies investigated the same population (children/adolescents). However, for half of the primary outcomes the evidence was downgraded by one level for inconsistency due to substantial/considerable statistical heterogeneity, and for most of the primary outcomes the evidence was downgraded by one level for imprecision because the low limit of the overall effect for the main analysis crossed the clinical threshold for relevance (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.1). For all primary outcomes the evidence was downgraded by one level for publication bias as visual analysis of the funnel plot did not indicate the presence or absence of publication bias. Therefore, the certainty of our estimates across primary outcomes was evaluated to be very low.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAmple evidence suggests an inverse association between HGS and morbidity and mortality [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Research on the association between changes in HGS and risk factors for lifestyle-related diseases in children and adolescents seems essential to clarify the mechanisms of the inverse association between HGS and morbidity/mortality [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This research aimed to systematically review and meta-analyze the current evidence for the association between HGS and markers of insulin resistance and inflammation in children and adolescents. The meta-analysis in this review used 11 of the 14 cross-sectional studies. However, insufficient information in the longitudinal studies made it impossible to pool data for associations between HGS change and the changes in insulin resistance and inflammatory markers.\u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eFinding from longitudinal studies\u003c/h2\u003e \u003cp\u003eThe results of this review indicate that no longitudinal studies with sufficient research information were found regarding the association between changes in HGS and changes in insulin resistance and inflammatory markers in children and adolescents. Therefore, we could not obtain evidence to elucidate the relationship between the two. However, several lifestyle-related factors, such as sports type [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], sleep duration [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and diets [\u003cspan additionalcitationids=\"CR48 CR49\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], are reported to impact HGS and markers of insulin resistance and inflammation in children and adolescents. A study reported that HGS, when measured in childhood, was associated with prediabetes or type-2 diabetes, similar to HGS measured in young adults and mid-adulthood [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. During the developmental period, HGS increases dramatically [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], and the changes in each individual are not uniform but show clear differences [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Furthermore, the results of the previous study described above [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] suppose that high HGS acquired during development favorably impacts markers of insulin resistance and may be associated with a lower incidence of diabetes. Therefore, future studies may elucidate the association between changes in HGS and changes in insulin resistance and inflammatory markers in children and adolescents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eFinding from cross-sectional studies\u003c/h2\u003e \u003cp\u003eThe results from cross-sectional studies indicated the association between HGS and several markers of insulin resistance and inflammation existed when studies utilized the relative HGS [Figures \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e]. However, no significant relationship was found when studies used absolute HGS [Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e]. The results from each study were adjusted for different factors such as age [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], sex [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], pubertal or maturation stage [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], physical activity [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], BMI or BMI-standard deviation score [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], school-type [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], and family background [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Unlike longitudinal studies that observe changes in HGS, participant's HGS in cross-sectional studies may be determined by complex factors, including being influenced by genetics [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and nongenetic factors [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], including in utero [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The same may apply to insulin resistance and inflammatory markers. At least a couple of possibilities exist for explaining the observed differences in the association of absolute and relative HGS with insulin resistance and inflammation markers.\u003c/p\u003e \u003cp\u003eFirstly, physical and sporting activities may be a possible factor. It is well known that physical and sporting activities impact insulin resistance and inflammatory markers [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] and HGS [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In addition, physical and sporting activities also control the accumulation of excess body fat, and there is an inverse association between accelerometer-measured physical activity and body fat mass in adolescents [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. A study compared the HGS of children divided into two groups based on recommended physical activity guidelines (\u0026gt;\u0026thinsp;60 minutes of moderate and vigorous physical activity per day) and found that HGS was similar between the two groups [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Even if daily physical activity does not influence HGS in children and adolescents who meet the recommended criteria for physical activity guidelines [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], physical activity-induced changes in body fat mass may affect body mass and relative HGS per body mass. On the other hand, the type of sports or physical activity (gripping tools with the hands or not) may influence the improvement of HGS in developing children and adolescents [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, it remains unclear how the type of sports or physical activity affects insulin resistance and inflammatory markers. Considering that differences in sport type (gripping tools with hands) influence the improvement of HGS and body composition in children and adolescents, investigating their effects on insulin resistance and inflammatory markers may help to understand the current study results.\u003c/p\u003e \u003cp\u003eThe quality and quantity of diets may be another factor influencing the association between absolute/relative HGS and insulin resistance and inflammatory markers. Poor dietary conditions may affect low HGS in children and adolescents [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. A study reported the possible association of higher culinary preparation intake with higher HGS and that of high ultra-processed food with lower HGS in male teenagers [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Longitudinal follow-up studies reported a possibility that there is an association between the high consumption of ultra-processed foods and greater whole-body and abdominal adiposity [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Furthermore, it is known that insulin resistance and C-reactive protein are associated with excess body fat [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], and overweight and obesity are causes of low relative HGS. Studies included in this review reported that the proportion of children and adolescents who were overweight/obese was approximately 10\u0026ndash;20% [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In contrast, no studies have controlled for the impact of body fat mass or percentage. Therefore, it remains possible that the influence of accumulated body fat on the reported results has yet to be excluded entirely.\u003c/p\u003e \u003cp\u003eLastly, the significance of the fact that the relative HGS divided by body mass, rather than the absolute value, was associated with insulin resistance and inflammatory markers is unclear. Therefore, it is not concluded from the meta-analysis results that high HGS is associated with desirable insulin resistance and inflammatory markers. The use of a ratio (HGS/body mass) can sometimes lead to spurious results [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. This might explain the discrepancy between absolute and relative HGS. Many include relatives because they are trying to \u0026ldquo;control\u0026rdquo; the denominator. However, this assumes that the numerator is scaled the same across all levels of the denominator. In order to elucidate the relationship between the two, it may be necessary to investigate the association between changes in HGS and marker changes in insulin resistance and inflammation in children and adolescents rather than cross-sectional studies. Future research is needed to resolve this issue.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eStrength and Limitations\u003c/h2\u003e \u003cp\u003eTo our knowledge, this is the first meta-analysis to provide an association between HGS and insulin resistance and inflammatory markers in children and adolescents. This review revealed that no longitudinal studies reported an association between the two. On the other hand, the cross-sectional studies used in the meta-analysis included studies with relatively large sample sizes. There are some limitations that need to be discussed. First, our meta-analysis results utilize results from cross-sectional studies, which limits the ability to determine the causality association between HGS and insulin resistance and inflammatory markers. Longitudinal follow-up research is needed to resolve these issues. Second, some studies have used BMI or pubertal status as surrogate markers of body fatness. Therefore, the results may differ when using the directly estimated body fat mass (or percentage) that impacts insulin resistance and inflammatory markers. Third, some included studies reported crude \u003cem\u003er\u003c/em\u003e values while others adjusted \u003cem\u003er\u003c/em\u003e values for sex and age and/or pubertal stage and body mass index, our results may be biased due confounding factors. Fourth, diet quality and balance may influence HGS and markers of insulin resistance inflammation, but almost no studies have controlled these effects. Fifth, most of the included studies in this review utilized HOMA-IR as an indicator of insulin resistance, with one study each utilizing QUICKI [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], which is a surrogate for glucose clamp-derived measures of insulin sensitivity, and oral glucose tolerance test (OGTT) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. One study [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] reported that the OGTT had a stronger relationship with relative HGS than the HOMA-IR, which may be helpful in future studies. Furthermore, it may be necessary to investigate the relationship with markers of protective effect against tissue inflammation. Sixth, some cross-sectional studies included in this systematic review were not included in the meta-analysis due insufficient information, and therefore, we strongly recommend future studies to also reporting \u003cem\u003er\u003c/em\u003e values for all correlation analyses. Seventh, due to the small number of studies included in each meta-analysis, we were unable to investigate the factors that could explain the significant statistical heterogeneity found for many analyses regarding relative HGS and insulin resistance and inflammatory markers. We were also unable to identify the presence or absence of publication bias due to the small number of studies included in each meta-analysis. Finally, the certainty of our estimates across primary outcomes was evaluated to be very low, which indicates a very uncertainty about the estimate. Thus, our results should be interpreted cautiously.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results from cross-sectional studies indicated the association (very low evidence) between HGS and several markers of insulin resistance and inflammation existed when studies utilized the relative HGS per body mass. However, no significant relationship was found when studies used absolute HGS. The reason for the difference in the results between absolute and relative HGS is unknown, but lifestyle-related factors that affect HGS and insulin resistance and inflammatory markers may be involved, and these may not be adequately controlled. Furthermore, as longitudinal studies were limited and did not include enough results to conduct a meta-analysis, future longitudinal follow-up studies are an important means of resolving these issues.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI body mass index\u003c/p\u003e\n\u003cp\u003eC3 complement components 3\u003c/p\u003e\n\u003cp\u003eC4 complement components 4\u003c/p\u003e\n\u003cp\u003eCRP C-reactive protein\u003c/p\u003e\n\u003cp\u003eGRADE certainty of evidence\u003c/p\u003e\n\u003cp\u003eHGS handgrip strength\u003c/p\u003e\n\u003cp\u003eHOMA-IR homeostatic model assessment for insulin resistance \u003c/p\u003e\n\u003cp\u003ehs-CRP high-sensitive C-reactive protein\u003c/p\u003e\n\u003cp\u003eIL-6 interleukin 6\u003c/p\u003e\n\u003cp\u003eOGTT oral glucose tolerance test\u003c/p\u003e\n\u003cp\u003eQUICKI quantitative insulin sensitivity check index\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplemental Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary materials to this article can be found in the attached file.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTA, RBV, AA, SM, HS, and JPL\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003econceived and designed the study. TA and RBV conducted the literature search, data extraction, and preliminary analysis. TA and RBV drafted the initial manuscript and performed statistical analysis. AA, SM, HS, and JPL revised and edited the manuscript. All authors approved the final version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by a grant from the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (Grant Number: JP22K11610).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was based on previously conducted studies and did not involve any new experiments with human participants or animals performed by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBoonpor J, Parra-Soto S, Petermann-Rocha F, Ferrari G, Welsh P, Pell JP, et al. Associations between Grip Strength and Incident Type 2 Diabetes: Findings from the UK Biobank Prospective Cohort Study. BMJ Open Diabetes Res Care. 2021;9(1):e001865. https://doi.org/10.1136/bmjdrc-2020-001865\u003c/li\u003e\n\u003cli\u003eLi G, Qiao Y, Lu Y, Liu S, Chen X, Ke C. Role of Handgrip Strength in Predicting New-Onset Diabetes: Findings from the Survey of Health, Ageing and Retirement in Europe. BMC Geriatr. 2021;21(1):445. https://doi.org/10.1186/s12877-021-02382-9\u003c/li\u003e\n\u003cli\u003ePeralta M, Dias CM, Marques A, Henriques-Neto D, Sousa-Uva M. Longitudinal Association between Grip Strength and the Risk of Heart Diseases among European Middle-Aged and Older Adults. Exp Gerontol. 2023;171:112014. https://doi.org/10.1016/j.exger.2022.112014\u003c/li\u003e\n\u003cli\u003eLopez-Bueno R, Andersen LL, Galatayud J, Casana J, Smith L, Jacob L, et al. Longitudinal association of handgrip strength with all-cause and cardiovascular mortality in older adults using a causal framework. Exp Gerontol. 2022;168:111951. https://doi.org/10.1016/j.exger.2022.111951\u003c/li\u003e\n\u003cli\u003eParra-Soto S, Pell JP, Celis-Morales C, Ho FK. Absolute and Relative Grip Strength as Predictors of Cancer: Prospective Cohort Study of 445552 Participants in UK Biobank. J Cachexia Sarcopenia Muscle. 2022;13:325\u0026ndash;32. https://doi.org/10.1002/jcsm.12863\u003c/li\u003e\n\u003cli\u003eLopez-Bueno R, Andersen LL, Calatayud J, Casana J, Grabovac I, Oberndorfer M, et al. Association of Handgrip Strength with All-Cause and Cancer Mortality in Older Adults: A Prospective Cohort Study in 28 Countries. Age Ageing. 2022;51:afac117. https://doi.org/10.1093/ageing/afac117\u003c/li\u003e\n\u003cli\u003eEsteban-Cornejo I, Ho FK, Petermann-Rocha F, Lyall DM, Martinez-Gomez D, Cabanas-Sanchez V, et al. Handgrip Strength and All-Cause Dementia Incidence and Mortality: Findings from the UK Biobank Prospective Cohort Study. J Cachexia Sarcopenia Muscle. 2022;13:1514\u0026ndash;25. https://doi.org/10.1002/jcsm.12857\u003c/li\u003e\n\u003cli\u003eDuchowny KA, Ackley SF, Brenowitz WD, Wang J, Zimmerman SC, Caunca MR, et al. Associations between Handgrip Strength and Dementia Risk, Cognition, and Neuroimaging Outcomes in the UK Biobank Cohort Study. JAMA Netw Open. 2022;5:e2218314. https://doi.org/10.1001/jamanetworkopen.2022.18314\u003c/li\u003e\n\u003cli\u003eMcGrath R, Clark BC, Cesari M, Johnson C, Jurivich DA. Handgrip Strength Asymmetry Is Associated with Future Falls in Older Americans. Aging Clin Exp Res. 2021;33:2461\u0026ndash;9. https://doi.org/10.1007/s40520-020-01757-z\u003c/li\u003e\n\u003cli\u003eWillems SM, Wright DJ, Day FR, Trajanoska K, Joshi PK, Morris JA, et al. Large-scale GWAS identifies multiple loci for hand grip strength providing biological insights into muscular fitness. Nat Commun. 2017;8:16015. https://doi.org/10.1038/ncomms16015\u003c/li\u003e\n\u003cli\u003eCelis-Morales CA, Lyall DM, Anderson J, Iliodromiti S, Fan Y, Ntuk UE, et al. The association between physical activity and risk of mortality is modulated by grip strength and cardiorespiratory fitness: evidence from 498135 UK-Biobank participants. Eur Heart J. 2017;38:116\u0026ndash;22. http://doi.org/10.1093/eurheart/ehw249\u003c/li\u003e\n\u003cli\u003eBohannon RW. Grip Strength: An Indispensable Biomarker for Older Adults. Clin Interv Aging. 2019;14:1681\u0026ndash;91. https://doi.org/10.2147/CIA.S194543\u003c/li\u003e\n\u003cli\u003eAbe T, Thiebaud SR, Ozaki H, Yamasaki S, Loenneke JP. Children with Low Handgrip Strength: A Narrative Review of Possible Exercise Strategies to Improve Its Development. Children. 2022;9(11):1616. https://doi.org/10.3390/children9111616\u003c/li\u003e\n\u003cli\u003eBuckner SL, Dankel SJ, Bell ZW, Abe T, Loenneke JP. The Association of Handgrip Strength and Mortality: What Does It Tell Us and What Can We Do with It? Rejuvenation Res. 2019;22(3):230\u0026ndash;4. https://doi.org/10.1089/rej.2018.2111\u003c/li\u003e\n\u003cli\u003eAbe T, Abe A, Loenneke JP. Handgrip Strength of Young Athletes Differs Based on the Type of Sport Played and Age. Am J Hum Biol. 2024;36(5):e24022. https://doi.org/10.1002/ajhb.24022\u003c/li\u003e\n\u003cli\u003eAbe T, Kohmura Y, Suzuki K, Someya Y, Loenneke LP, Machida S, et al. Athletes in Sporting Events with Upper-Body Gripping Movements Have Greater Handgrip Strength than Those in Sporting Events That Prioritize the Lower Body. Am J Hum Biol. 2023;35(7):e23891. https://doi.org/10.1002/ajhb.23891\u003c/li\u003e\n\u003cli\u003eAbe T, Kohmura Y, Suzuki K, Someya Y, Loenneke JP, Machida S, et al. Handgrip Strength and Healthspan: Impact of Sports during the Developmental Period on Handgrip Strength (Juntendo Fitness plus Study). Juntendo Med J. 2023;69(5):400\u0026ndash;4. https://doi.org/10.14789/jmj.JMJ23-0017-P\u003c/li\u003e\n\u003cli\u003eBuckner SL, Dankel SJ, Mouser JG, Mattocks KT, Jessee MB, Loenneke JP. Chasing the top quartile of cross-sectional data: Is it possible with resistance training? Med Hypotheses. 2017;108:63\u0026ndash;8. https://doi.org/10.1016/j.mehy.2017.08.009\u003c/li\u003e\n\u003cli\u003eAbe T, Viana RB, Dankel SJ, Loenneke JP. Different resistance exercise interventions for handgrip strength in apparently healthy adults: A systematic review. Int J Clin Med. 2023;14(12):552\u0026ndash;81. https://doi.org/10.4236/ijcm.2023.1412047\u003c/li\u003e\n\u003cli\u003eSmith L, Yang L, Hamer M. Handgrip strength, inflammatory markers, and mortality. Scand J Med Sci Sports. 2019;29(8):1190\u0026ndash;6. https://doi.org/10.1111/sms.13433\u003c/li\u003e\n\u003cli\u003eTuttle CSL, Thang LAN, Maier AB. Markers of inflammation and their association with muscle strength and mass: A systematic review and meta-analysis. Ageing Res Rev. 2020;64:101185. http://doi.org/10.1016/j.arr.2020.101185\u003c/li\u003e\n\u003cli\u003ePage MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement. J Clin Epidemiol. 2021;134:103\u0026ndash;12. doi: 10.1016/j.jclinepi.2021.02.003 \u003c/li\u003e\n\u003cli\u003eJoanna Briggs Institute. \u003cem\u003eCritical appraisal tools, 2020\u003c/em\u003e. https://jbi.global/critical-appraisal-tools\u003c/li\u003e\n\u003cli\u003eGuyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008;336(7650):924\u0026ndash;6. https://doi.org/10.1136/bmj.39489.470347.AD \u003c/li\u003e\n\u003cli\u003ePeterson RA, Brown SP. On the use of beta coefficients in meta-analysis. J Appl Psychol. 2005;90(1):175\u0026ndash;81. https://doi.org/10.1037/0021-9010.90.1.175 \u003c/li\u003e\n\u003cli\u003eBorenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to meta-analysis. In \u003cem\u003eIntroduction to Meta-Analysis\u003c/em\u003e. John Wiley and Sons, 2009. https://doi.org/10.1002/9780470743386\u003c/li\u003e\n\u003cli\u003eMunro BH. \u003cem\u003eStatistical Methods for Health Care Research\u003c/em\u003e. JB Lippincott, 1986.\u003c/li\u003e\n\u003cli\u003eHiggins J, Thomas J, Chandler J, Cumpston M, Li T, Page M, et al. \u003cem\u003eCochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023)\u003c/em\u003e (J. Chandler, M. Cumpston, T. Li, M. Page, \u0026amp; V. Welch (eds.)). Cochrane, 2023. www.training.cochrane.org/handbook\u003c/li\u003e\n\u003cli\u003eJASP Team. JASP (Version 0.18.3)[Computer software], 2024.\u003c/li\u003e\n\u003cli\u003eAgostinis-Sobrinho CA, Moreira C, Abreu S, Lopes L, Sardinha LB, Oliveira-Santos J, et al. Muscular fitness and metabolic and inflammatory biomarkers in adolescents: results from LabMed physical activity study. Scand J Med Sci Sports. 2017;27(12):1873\u0026ndash;80. https://doi.org/10.1111/sms.12805 \u003c/li\u003e\n\u003cli\u003eArtero EG, Espa\u0026ntilde;a-Romero V, Jim\u0026eacute;nez-Pav\u0026oacute;n D, Martinez-G\u0026oacute;mez D, Warnberg J, G\u0026oacute;mez-Mart\u0026iacute;nez S, et al. Muscular fitness, fatness and inflammatory biomarkers in adolescents. Pediatr Obesity. 2014;9(5):391\u0026ndash;400. https://doi.org/10.1111/j.2047-6310.2013.00186.x \u003c/li\u003e\n\u003cli\u003eArtero EG, Ruiz JR, Ortega FB, Espa\u0026ntilde;a-Romero V, Vicente-Rodr\u0026iacute;guez G, Molnar D, et al. Muscular and cardiorespiratory fitness are independently associated with metabolic risk in adolescents: The HELENA study. Pediatr Diabetes. 2011;12(8):704\u0026ndash;12. https://doi.org/10.1111/j.1399-5448.2011.00769.x \u003c/li\u003e\n\u003cli\u003eCastro-Pi\u0026ntilde;ero J, Laurson KR, Artero EG, Ortega FB, Labayen I, Ruperez AI, et al. Muscle strength field-based tests to identify European adolescents at risk of metabolic syndrome: The HELENA study. J Sci Med Sport. 2019;22(8):929\u0026ndash;34. https://doi.org/10.1016/j.jsams.2019.04.008 \u003c/li\u003e\n\u003cli\u003eCohen DD, G\u0026oacute;mez-Arbel\u0026aacute;ez D, Camacho PA, Pinzon S, Hormiga C, Trejos-Suarez J, et al. Low muscle strength is associated with metabolic risk factors in Colombian children: The ACFIES study. PLoS ONE. 2014;9(4):1\u0026ndash;10. https://doi.org/10.1371/journal.pone.0093150 \u003c/li\u003e\n\u003cli\u003eDelgado-Alfonso A, P\u0026eacute;rez-Bey A, Conde-Caveda J, Izquierdo-G\u0026oacute;mez R, Esteban-Cornejo I, G\u0026oacute;mez-Mart\u0026iacute;nez S, et al. Independent and combined associations of physical fitness components with inflammatory biomarkers in children and adolescents. Pediatr Res. 2018;84(5):704\u0026ndash;12. https://doi.org/10.1038/s41390-018-0150-5 \u003c/li\u003e\n\u003cli\u003eHaapala EA, Kuronen E, Ihalainen JK, Lintu N, Lepp\u0026auml;nen MH, Tompuri T, et al. Cross-sectional associations between physical fitness and biomarkers of inflammation in children\u0026mdash;The PANIC study. Scand J Med Sci Sports. 2023;33(6):1000\u0026ndash;9. https://doi.org/10.1111/sms.14337 \u003c/li\u003e\n\u003cli\u003eJim\u0026eacute;nez-Pav\u0026oacute;n D, Ortega FB, Valtue\u0026ntilde;a J, Castro-Pi\u0026ntilde;ero J, G\u0026oacute;mez-Mart\u0026iacute;nez S, Zaccaria M, et al. (2012). Muscular strength and markers of insulin resistance in European adolescents: The HELENA Study. Eur J Appl Physiol. 2012;112(7):2455\u0026ndash;65. https://doi.org/10.1007/s00421-011-2216-5 \u003c/li\u003e\n\u003cli\u003eJung HW, Lee J, Kim J. Handgrip strength is associated with metabolic syndrome and insulin resistance in children and adolescents: analysis of Korea national health and nutrition examination survey 2014-2018. J Obes Metab Syndr. 2022;31(4):334\u0026ndash;44. https://doi.org/10.7570/jomes22053 \u003c/li\u003e\n\u003cli\u003eLang JJ, Larouche R, Tremblay MS. The association between physical fitness and health in a nationally representative sample of Canadian children and youth aged 6 to 17 years. Health Promot Chronic Dis Prev Can. 2019;39(3):104\u0026ndash;11. https://doi.org/10.24095/hpcdp.39.3.02 \u003c/li\u003e\n\u003cli\u003eLi S, Zhang R, Pan G, Zheng L, Li C. Handgrip strength is associated with insulin resistance and glucose metabolism in adolescents: evidence from national health and nutrition examination survey 2011 to 2014. Pediatr Diabetes. 2018;19(3):375\u0026ndash;80. https://doi.org/10.1111/pedi.12596 \u003c/li\u003e\n\u003cli\u003eL\u0026oacute;pez-Gil JF, Weisstaub G, Ram\u0026iacute;rez-V\u0026eacute;lez R, Garc\u0026iacute;a-Hermoso A. Handgrip strength cut-off points for early detection of cardiometabolic risk in Chilean children. Eur J Pediatr. 2021;180(12):3483\u0026ndash;9. https://doi.org/10.1007/s00431-021-04142-8 \u003c/li\u003e\n\u003cli\u003eTarp J, Bugge A, M\u0026oslash;ller NC, Klakk H, Rexen CT, Gr\u0026oslash;ntved A, et al. Muscle fitness changes during childhood associates with improvements in cardiometabolic risk factors: a prospective study. J Phys Act Health. 2019;16(2):108\u0026ndash;15. https://doi.org/10.1123/jpah.2017-0678 \u003c/li\u003e\n\u003cli\u003eZaqout M, Michels N, Bammann K, Ahrens W, Sprengeler O, Molnar D, et al. Influence of physical fitness on cardio-metabolic risk factors in European children. The IDEFICS study. Int J Obes. 2016;40(7):1119\u0026ndash;25. https://doi.org/10.1038/ijo.2016.22\u003c/li\u003e\n\u003cli\u003eDemmer DL, Beilin LJ, Hands B, Burrows S, Cox KL, Straker LM, et al. Effects of muscle strength and endurance on blood pressure and related cardiometabolic risk factors from childhood to adolescence. J Hypertens. 2016;34(12):2365\u0026ndash;75. https://doi.org/10.1097/HJH.0000000000001116\u003c/li\u003e\n\u003cli\u003eAbe T, Viana RB, Machida S, Loenneke JP. Impact of sports type on handgrip strength and morbidity/mortality. J Trainology. 2024;13(1):1\u0026ndash;2. https://www.jstage.jst.go.jp/article/trainology/13/1/13_1/_article/-char/en\u003c/li\u003e\n\u003cli\u003eMorales-Munoz I, Marwaha S, Upthegrove R, Cropley V. Role of inflammation in short sleep duration across childhood and psychosis in young adulthood. JAMA Psychiatry. 2024;81(8):825\u0026ndash;83. https://doi.org/10.1001/jamapsychiatry.2024.0796\u003c/li\u003e\n\u003cli\u003eBim MA, de Araujo Pinto A, Scarabelot KS, Claumann GS, Pelegrini A. Handgrip strength and associated factors among Brazilian adolescents: A cross-sectional study. J Bodyw Mov Ther. 2021;28:75\u0026ndash;81. https://doi.org/10.1016/j.jbmt.2021.06.010\u003c/li\u003e\n\u003cli\u003eConfortin SC, Barbosa AR, de Oliveira BR, da Silva Magalhaes EI, Braganca MLBM, de Britto e Alves MTSS, et al. The consumption of culinary preparations and ultra-processed food is associated with handgrip strength in teenagers. Nutr J. 2022;21(1):66. https://doi.org/10.1186/s12937-022-00818-5\u003c/li\u003e\n\u003cli\u003eBordoni A, Danesi F, Dardevet D, Dupont D, Fernandez AS, Gille D, et al. Dairy products and inflammation: A review of the clinical evidence. Crit Rev Food Sci Nutr. 2017;57(12):2497\u0026ndash;525. https://doi.org/10.1080/10408398.2014.967385\u003c/li\u003e\n\u003cli\u003eRomero-Polvo A, Denova-Gutierrez E, Rivera-Paredez B, Castanon S, Gallegos-Carrillo K, Halley-Castillo E, et al. Association between dietary patterns and insulin resistance in Mexican children and adolescents. Ann Nutr Metab. 2012;61(2):142\u0026ndash;50. https://doi.org/10.1159/000341493\u003c/li\u003e\n\u003cli\u003eFraser BJ, Blizzard L, Buscot M J, Schmidt MD, Dwyer T, Venn AJ, et al. The association between grip strength measured in childhood, young- and mid-adulthood and prediabetes or type 2 diabetes in mid-adulthood. Sports Med. 2021;51(1):175\u0026ndash;83. https://doi.org/10.1007/s40279-020-01328-2\u003c/li\u003e\n\u003cli\u003eBohannon RW, Wang Y-C, Bubela D, Gershon RC. Handgrip strength: A population-based study of norms and age trajectories for 3- to 17-year-olds. Pediatr Phys Ther. 2017;29(2):118\u0026ndash;23. https://doi.org/10.1097/PEP.0000000000000366\u003c/li\u003e\n\u003cli\u003eAbe A, Sanui R, Loenneke JP, Abe T. One-year handgrip strength change in kindergarteners depends upon physical activity status. Life (Basel). 2023;13(8):1665. https://doi.org/10.3390/life13081665\u003c/li\u003e\n\u003cli\u003eSayer AA, Syddall HE, Gilbody HJ, Dennison EM, Cooper C. Does sarcopenia originate in early life? Finding from the Heartfordshire Cohort Study. J Gerontol A Biol Sci Med Sci. 2004;59(9):M930\u0026ndash;4. https://10.1093/gerona/59.9.m930\u003c/li\u003e\n\u003cli\u003eAbe T, Abe A, Loenneke JP. Association of changes in grip strength with second digit length adjusted for fourth digit length in young children. Am J Hum Biol. 2023;35(8):e23901. https://doi.org/10.1002/ajhb.23901\u003c/li\u003e\n\u003cli\u003eHardin DS, Azzarelli B, Edwards J, Wigglesworth J, Maianu L, Brechtel G, et al. Mechanisms of enhanced insulin sensitivity in endurance-trained athletes: Effects on blood flow and differential expression of GLUT 4 in skeletal muscles. J Clin Endocrinol Metab. 1995;80(8):2437\u0026ndash;46. https://doi.org/10.1210/jcem.80.8.7629239\u003c/li\u003e\n\u003cli\u003eKing DE, Carek P, Mainous III AG, Pearson WS. Inflammatory markers and exercise: Differences related to exercise type. Med Sci Sports Exerc. 2003;35(4):575\u0026ndash;81. https://doi.org/10.1249/01.MSS.0000058440.28108.CC\u003c/li\u003e\n\u003cli\u003eBasterfield L, Reilly JK, Pearce MS, Parkinson KN, Adamson AJ, Reilly JJ, et al. Longitudinal associations between sports participation, body composition and physical activity from childhood to adolescence. J Sci Med Sport. 2015;18(2):178\u0026ndash;82. http://dx.doi.org/10.1016/j.jsams.2014.03.005\u003c/li\u003e\n\u003cli\u003eTanaka C, Tremblay MS, Okuda M, Tanaka S. Association between 24-hour movement guidelines and physical fitness in children. Pediatr Int. 2020;62(12):1381\u0026ndash;7. https://doi.org/10.1111/ped.14322\u003c/li\u003e\n\u003cli\u003eTapia-Serrano MA, Lopez-Gil JF, Sevil-Serrano J, Garcia-Hermoso A, Sanchez-Miguel PA. What is the role of adherence to 24-hour movement guidelines in relation to physical fitness components among adolescents? Scand J Med Sci Sports. 2023;33(8):1373\u0026ndash;83. https://doi.org/10.1111/sms.14357\u003c/li\u003e\n\u003cli\u003eDe Amicis R, Mambrini SP, Pellizzari M, Foppiani A, Bertoli S, Battezzati A, et al. Ultra-processed foods and obesity and adiposity parameters among children and adolescents: a systematic review. Eur J Nutr. 2022;61(5):2297\u0026ndash;311. https://doi.org/10.1007/s00394-022-02873-4\u003c/li\u003e\n\u003cli\u003eMaffeis C, Morandi A. Body composition and insulin resistance in children. Eur J Clin Nutr. 2018;72(9):1239\u0026ndash;45. https://doi.org/10.1038/s41430-018-0239-2\u003c/li\u003e\n\u003cli\u003eNavarro P, de Dios O, Gavela-Perez T, Jois A, Garcea C, Soriano-Guillen L. High-sensitivity C-reactive protein and leptin levels related to body mass index changes throughout childhood. J Pediatr. 2016;178:178\u0026ndash;82. https://doi.org/10.1016/j.jpeds.2016.08.020\u003c/li\u003e\n\u003cli\u003eCurran-Everett D. Explorations in statistics: the analysis of ratios and normalized data. Adv Physiol Educ. 2013;37(3):213\u0026ndash;9. https://doi.org/10.1152/advan.00053.2013\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"grip strength, biomarkers, c-reactive protein, glucose metabolism, pediatrics","lastPublishedDoi":"10.21203/rs.3.rs-6291913/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6291913/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eResearch on the association between changes in handgrip strength (HGS) and risk factors for lifestyle-related diseases in children and adolescents is essential to clarify the inverse association between HGS and morbidity/mortality mechanisms. This systematic review with meta-analysis aimed to investigate the cross-sectional and longitudinal associations between HGS and markers of insulin resistance and inflammation in children and adolescents. Observational studies that investigated the cross-sectional or/and longitudinal associations between HGS and markers of insulin resistance and inflammation in children and adolescents were searched. Summary effect size measures were calculated using a random-effects model estimation and reported as Fisher\u0026rsquo;s r-to-z transformed correlation coefficients and 95% confidence intervals. Fifteen studies (12 cross-sectional, two cross-sectional and longitudinal, and one longitudinal) were included in the systematic review, of which 11 studies were also included in the meta-analyses for cross-sectional correlation. Relative (per body mass) but not absolute HGS was significantly associated (very low evidence) with markers of insulin resistance. Relative HGS was also significantly associated (very low evidence) with most of the inflammatory markers investigated. The three longitudinal studies included had insufficient information to perform a meta-analysis. The results from cross-sectional studies indicated the association (very low evidence) between HGS and several markers of insulin resistance and inflammation existed when studies utilized the relative HGS per body mass. However, no significant relationship was found when studies used absolute HGS. Furthermore, as longitudinal studies were limited, future longitudinal follow-up studies are an important means of resolving these issues.\u003c/p\u003e","manuscriptTitle":"Associations between handgrip strength and markers of insulin resistance and inflammation in childhood and adolescence: A systematic review with meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-23 07:48:59","doi":"10.21203/rs.3.rs-6291913/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ee4d3372-e858-4a77-9d59-f110388b46bb","owner":[],"postedDate":"June 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T07:48:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-23 07:48:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6291913","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6291913","identity":"rs-6291913","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}