Comparison of Quality of Carbohydrate Metrics Related to Fasting Insulin, Glycosylated Hemoglobin and HOMA-IR in Brazilian Adolescents
Low glycemic index (GI) and glycemic load (GL) diets are effective for glycemic control (GC) associated with a carbohydrate-controlled meal plan. However, whether GI and GL peaks are related to GC is unknown. Objective: To compare the daily GI (DGI)/GL (DGL) and average GI (AvGI)/GL (AvGL) of meals...
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| Veröffentlicht in: | Nutrients 2022-06, Vol.14 (12), p.2544 |
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| description | Low glycemic index (GI) and glycemic load (GL) diets are effective for glycemic control (GC) associated with a carbohydrate-controlled meal plan. However, whether GI and GL peaks are related to GC is unknown. Objective: To compare the daily GI (DGI)/GL (DGL) and average GI (AvGI)/GL (AvGL) of meals (accounting for peaks) related to GC markers (GCM) in Brazilian adolescents. Methods: A representative national school-based (public/private) sample of students without diabetes, 12−17 years of age, was evaluated. Food intake was based on a 24 h recall. The models for complex cluster sampling were adjusted (sex, sexual maturation, age, and physical activity). Results: Of 35,737 students, 74% were from public schools, 60% girls, 17% overweight, and 8% obese. The minimum DGI and DGL were observed at lunch, with higher values at night. Fasting insulin was 1.5 times higher in overweight/obese (OW) girls, and 1.7 times higher in OW boys than in normal-weight (NW) girls. The same trend was observed for the homeostatic model assessment for insulin resistance (HOMA-IR) (OW = 2.82 vs. NW = 1.84 in girls; OW = 2.66 vs. NW = 1.54 in boys; p < 0.05). The daily and average metrics were greater for NW adolescents. Glycosylated hemoglobin was not associated with these metrics, except for AvGL. Insulin and HOMA-IR were associated with all metrics in NW adolescents, with greater coefficients associated with AvGL. Among overweight/obese adolescents, only GI metrics were associated (β = 0.23; AvGI and insulin) and appeared to have the best association with GCM. Conclusions: Among NW adolescents, GL is a better measure of carbohydrate quality, but for those with overweight/obesity, carbohydrate consumption is more associated with GC, probably because they eat/report small amounts of carbohydrates. |
| doi_str_mv | 10.3390/nu14122544 |
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However, whether GI and GL peaks are related to GC is unknown. Objective: To compare the daily GI (DGI)/GL (DGL) and average GI (AvGI)/GL (AvGL) of meals (accounting for peaks) related to GC markers (GCM) in Brazilian adolescents. Methods: A representative national school-based (public/private) sample of students without diabetes, 12−17 years of age, was evaluated. Food intake was based on a 24 h recall. The models for complex cluster sampling were adjusted (sex, sexual maturation, age, and physical activity). Results: Of 35,737 students, 74% were from public schools, 60% girls, 17% overweight, and 8% obese. The minimum DGI and DGL were observed at lunch, with higher values at night. Fasting insulin was 1.5 times higher in overweight/obese (OW) girls, and 1.7 times higher in OW boys than in normal-weight (NW) girls. The same trend was observed for the homeostatic model assessment for insulin resistance (HOMA-IR) (OW = 2.82 vs. NW = 1.84 in girls; OW = 2.66 vs. NW = 1.54 in boys; p < 0.05). The daily and average metrics were greater for NW adolescents. Glycosylated hemoglobin was not associated with these metrics, except for AvGL. Insulin and HOMA-IR were associated with all metrics in NW adolescents, with greater coefficients associated with AvGL. Among overweight/obese adolescents, only GI metrics were associated (β = 0.23; AvGI and insulin) and appeared to have the best association with GCM. Conclusions: Among NW adolescents, GL is a better measure of carbohydrate quality, but for those with overweight/obesity, carbohydrate consumption is more associated with GC, probably because they eat/report small amounts of carbohydrates.</description><identifier>ISSN: 2072-6643</identifier><identifier>EISSN: 2072-6643</identifier><identifier>DOI: 10.3390/nu14122544</identifier><identifier>PMID: 35745274</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Adolescent ; Adolescents ; Benchmarking ; Blood Glucose ; Body weight ; Brazil ; Carbohydrates ; Child ; Diabetes mellitus ; Dietary Carbohydrates ; Fasting ; Female ; Food and nutrition ; Food intake ; Glycated Hemoglobin ; Glycemic Index ; Health aspects ; Hemoglobin ; Humans ; Insulin ; Insulin Resistance ; Male ; Meals ; Measurement ; Obesity ; Overweight ; Physical activity ; Physiological aspects ; Schools ; Students ; Teenagers ; Youth</subject><ispartof>Nutrients, 2022-06, Vol.14 (12), p.2544</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 by the authors. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c473t-e717a155709de8032bd84f4b8fc34af07ec12c282e425d438980919385e3763e3</citedby><cites>FETCH-LOGICAL-c473t-e717a155709de8032bd84f4b8fc34af07ec12c282e425d438980919385e3763e3</cites><orcidid>0000-0002-1101-8746 ; 0000-0002-3513-2084 ; 0000-0003-1720-3560 ; 0000-0001-9261-4250</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9227805/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9227805/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35745274$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>da Rocha, Camilla Medeiros Macedo</creatorcontrib><creatorcontrib>Gama, Vanessa Proêza Maciel</creatorcontrib><creatorcontrib>de Moura Souza, Amanda</creatorcontrib><creatorcontrib>Massae Yokoo, Edna</creatorcontrib><creatorcontrib>Verly Junior, Eliseu</creatorcontrib><creatorcontrib>Bloch, Katia Vergetti</creatorcontrib><creatorcontrib>Sichieri, Rosely</creatorcontrib><title>Comparison of Quality of Carbohydrate Metrics Related to Fasting Insulin, Glycosylated Hemoglobin and HOMA-IR in Brazilian Adolescents</title><title>Nutrients</title><addtitle>Nutrients</addtitle><description>Low glycemic index (GI) and glycemic load (GL) diets are effective for glycemic control (GC) associated with a carbohydrate-controlled meal plan. However, whether GI and GL peaks are related to GC is unknown. Objective: To compare the daily GI (DGI)/GL (DGL) and average GI (AvGI)/GL (AvGL) of meals (accounting for peaks) related to GC markers (GCM) in Brazilian adolescents. Methods: A representative national school-based (public/private) sample of students without diabetes, 12−17 years of age, was evaluated. Food intake was based on a 24 h recall. The models for complex cluster sampling were adjusted (sex, sexual maturation, age, and physical activity). Results: Of 35,737 students, 74% were from public schools, 60% girls, 17% overweight, and 8% obese. The minimum DGI and DGL were observed at lunch, with higher values at night. Fasting insulin was 1.5 times higher in overweight/obese (OW) girls, and 1.7 times higher in OW boys than in normal-weight (NW) girls. The same trend was observed for the homeostatic model assessment for insulin resistance (HOMA-IR) (OW = 2.82 vs. NW = 1.84 in girls; OW = 2.66 vs. NW = 1.54 in boys; p < 0.05). The daily and average metrics were greater for NW adolescents. Glycosylated hemoglobin was not associated with these metrics, except for AvGL. Insulin and HOMA-IR were associated with all metrics in NW adolescents, with greater coefficients associated with AvGL. Among overweight/obese adolescents, only GI metrics were associated (β = 0.23; AvGI and insulin) and appeared to have the best association with GCM. 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Gama, Vanessa Proêza Maciel ; de Moura Souza, Amanda ; Massae Yokoo, Edna ; Verly Junior, Eliseu ; Bloch, Katia Vergetti ; Sichieri, Rosely</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c473t-e717a155709de8032bd84f4b8fc34af07ec12c282e425d438980919385e3763e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Adolescent</topic><topic>Adolescents</topic><topic>Benchmarking</topic><topic>Blood Glucose</topic><topic>Body weight</topic><topic>Brazil</topic><topic>Carbohydrates</topic><topic>Child</topic><topic>Diabetes mellitus</topic><topic>Dietary Carbohydrates</topic><topic>Fasting</topic><topic>Female</topic><topic>Food and nutrition</topic><topic>Food intake</topic><topic>Glycated Hemoglobin</topic><topic>Glycemic Index</topic><topic>Health aspects</topic><topic>Hemoglobin</topic><topic>Humans</topic><topic>Insulin</topic><topic>Insulin Resistance</topic><topic>Male</topic><topic>Meals</topic><topic>Measurement</topic><topic>Obesity</topic><topic>Overweight</topic><topic>Physical activity</topic><topic>Physiological aspects</topic><topic>Schools</topic><topic>Students</topic><topic>Teenagers</topic><topic>Youth</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>da Rocha, Camilla Medeiros Macedo</creatorcontrib><creatorcontrib>Gama, Vanessa Proêza Maciel</creatorcontrib><creatorcontrib>de Moura Souza, Amanda</creatorcontrib><creatorcontrib>Massae Yokoo, Edna</creatorcontrib><creatorcontrib>Verly Junior, Eliseu</creatorcontrib><creatorcontrib>Bloch, Katia Vergetti</creatorcontrib><creatorcontrib>Sichieri, Rosely</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Physical Education Index</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Nutrients</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>da Rocha, Camilla Medeiros Macedo</au><au>Gama, Vanessa Proêza Maciel</au><au>de Moura Souza, Amanda</au><au>Massae Yokoo, Edna</au><au>Verly Junior, Eliseu</au><au>Bloch, Katia Vergetti</au><au>Sichieri, Rosely</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparison of Quality of Carbohydrate Metrics Related to Fasting Insulin, Glycosylated Hemoglobin and HOMA-IR in Brazilian Adolescents</atitle><jtitle>Nutrients</jtitle><addtitle>Nutrients</addtitle><date>2022-06-19</date><risdate>2022</risdate><volume>14</volume><issue>12</issue><spage>2544</spage><pages>2544-</pages><issn>2072-6643</issn><eissn>2072-6643</eissn><abstract>Low glycemic index (GI) and glycemic load (GL) diets are effective for glycemic control (GC) associated with a carbohydrate-controlled meal plan. However, whether GI and GL peaks are related to GC is unknown. Objective: To compare the daily GI (DGI)/GL (DGL) and average GI (AvGI)/GL (AvGL) of meals (accounting for peaks) related to GC markers (GCM) in Brazilian adolescents. Methods: A representative national school-based (public/private) sample of students without diabetes, 12−17 years of age, was evaluated. Food intake was based on a 24 h recall. The models for complex cluster sampling were adjusted (sex, sexual maturation, age, and physical activity). Results: Of 35,737 students, 74% were from public schools, 60% girls, 17% overweight, and 8% obese. The minimum DGI and DGL were observed at lunch, with higher values at night. Fasting insulin was 1.5 times higher in overweight/obese (OW) girls, and 1.7 times higher in OW boys than in normal-weight (NW) girls. The same trend was observed for the homeostatic model assessment for insulin resistance (HOMA-IR) (OW = 2.82 vs. NW = 1.84 in girls; OW = 2.66 vs. NW = 1.54 in boys; p < 0.05). The daily and average metrics were greater for NW adolescents. Glycosylated hemoglobin was not associated with these metrics, except for AvGL. Insulin and HOMA-IR were associated with all metrics in NW adolescents, with greater coefficients associated with AvGL. Among overweight/obese adolescents, only GI metrics were associated (β = 0.23; AvGI and insulin) and appeared to have the best association with GCM. Conclusions: Among NW adolescents, GL is a better measure of carbohydrate quality, but for those with overweight/obesity, carbohydrate consumption is more associated with GC, probably because they eat/report small amounts of carbohydrates.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>35745274</pmid><doi>10.3390/nu14122544</doi><orcidid>https://orcid.org/0000-0002-1101-8746</orcidid><orcidid>https://orcid.org/0000-0002-3513-2084</orcidid><orcidid>https://orcid.org/0000-0003-1720-3560</orcidid><orcidid>https://orcid.org/0000-0001-9261-4250</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adolescent Adolescents Benchmarking Blood Glucose Body weight Brazil Carbohydrates Child Diabetes mellitus Dietary Carbohydrates Fasting Female Food and nutrition Food intake Glycated Hemoglobin Glycemic Index Health aspects Hemoglobin Humans Insulin Insulin Resistance Male Meals Measurement Obesity Overweight Physical activity Physiological aspects Schools Students Teenagers Youth |
| title | Comparison of Quality of Carbohydrate Metrics Related to Fasting Insulin, Glycosylated Hemoglobin and HOMA-IR in Brazilian Adolescents |
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