{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T08:08:38Z","timestamp":1778573318749,"version":"3.51.4"},"reference-count":53,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Diabetes Sci Technol"],"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>\n                      Hemoglobin A\n                      <jats:sub>1C<\/jats:sub>\n                      (HbA\n                      <jats:sub>1C<\/jats:sub>\n                      ) is the gold standard for assessing long-term glycemic control in people with diabetes. Increasing use of continuous glucose monitoring (CGM) has led to adoption of the glucose management indicator (GMI) as a CGM\u2011based HbA\n                      <jats:sub>1C<\/jats:sub>\n                      estimate, but GMI often differs from laboratory HbA\n                      <jats:sub>1C<\/jats:sub>\n                      , especially in type 2 diabetes. This discordance may be associated with the fact that GMI, as a measure of central tendency, fails to capture temporal glycemic trends and variability that relate to HbA\n                      <jats:sub>1C<\/jats:sub>\n                      formation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>\n                      To evaluate whether combining CGM-derived metrics capturing variability, excursions, and temporal trends improves estimation of laboratory-measured HbA\n                      <jats:sub>1C<\/jats:sub>\n                      in type 2 diabetes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>\n                      A machine learning framework was applied to CGM data from a three-month randomized trial, including 159 participants with type 2 diabetes. Participants had \u226570% CGM data coverage and valid end-of-trial HbA\n                      <jats:sub>1C<\/jats:sub>\n                      . From a standardized 90-day CGM window, 51 metrics were extracted. Benchmark models (mean glucose and GMI) were compared with models developed using forward and exhaustive feature selection with threefold cross-validated multiple linear regression.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      Benchmark models yielded\n                      <jats:italic toggle=\"yes\">R<\/jats:italic>\n                      -squared = 0.53. A forward selection model including five metrics (GMI at night, night-to-overall mean glucose ratio, glycemic risk assessment diabetes equation, time in tight range [3.0-7.8 mmol\/L], time above range [13.9 mmol\/L] at night) improved\n                      <jats:italic toggle=\"yes\">R<\/jats:italic>\n                      -squared to 0.60. The best-performing model (substituting GRADE at night for GMI at night) achieved a similar\n                      <jats:italic toggle=\"yes\">R<\/jats:italic>\n                      -squared (0.61). Nighttime and hyperglycemia\u2011related metrics were consistently selected.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>\n                      Continuous glucose monitoring\u2011based HbA\n                      <jats:sub>1C<\/jats:sub>\n                      estimation improves when variability and temporal patterns are included. Nighttime hyperglycemia adds notable predictive value, though further validation is needed.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/19322968261441312","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T11:18:42Z","timestamp":1778239122000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond the Mean: A Machine Learning-Based Trend Analysis of CGM Metrics for Improved HbA\n                    <sub>1C<\/sub>\n                    Estimation in Type 2 Diabetes"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6229-7282","authenticated-orcid":false,"given":"Camilla H. N.","family":"Thomsen","sequence":"first","affiliation":[{"name":"Department of Health Science and Technology, Aalborg University, Gistrup, Denmark"},{"name":"Steno Diabetes Center North Denmark, Aalborg, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3484-7571","authenticated-orcid":false,"given":"Simon L.","family":"Cichosz","sequence":"additional","affiliation":[{"name":"Department of Health Science and Technology, Aalborg University, Gistrup, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2693-0527","authenticated-orcid":false,"given":"Thomas","family":"Kronborg","sequence":"additional","affiliation":[{"name":"Department of Health Science and Technology, Aalborg University, Gistrup, Denmark"},{"name":"Steno Diabetes Center North Denmark, Aalborg, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0395-3563","authenticated-orcid":false,"given":"Stine","family":"Hangaard","sequence":"additional","affiliation":[{"name":"Department of Health Science and Technology, Aalborg University, Gistrup, Denmark"},{"name":"Steno Diabetes Center North Denmark, Aalborg, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Vestergaard","sequence":"additional","affiliation":[{"name":"Steno Diabetes Center North Denmark, Aalborg, Denmark"},{"name":"Department of Endocrinology, Aalborg University Hospital, Aalborg, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1675-8417","authenticated-orcid":false,"given":"Irl B.","family":"Hirsch","sequence":"additional","affiliation":[{"name":"University of Washington Diabetes Institute, Seattle, WA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6394-6862","authenticated-orcid":false,"given":"David C.","family":"Klonoff","sequence":"additional","affiliation":[{"name":"Diabetes Research Institute, Mills\u2013Peninsula Health Services, San Mateo, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6649-8644","authenticated-orcid":false,"given":"Morten H.","family":"Jensen","sequence":"additional","affiliation":[{"name":"Department of Health Science and Technology, Aalborg University, Gistrup, Denmark"},{"name":"Data Science, Novo Nordisk A\/S, S\u00f8borg, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.2337\/dci22-0034"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.4137\/BMI.S38440"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.diabres.2025.112111"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.2337\/cd20-0043"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2020.0501"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.2337\/dc18-1581"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12020-024-04083-w"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1177\/2042018820931664"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1111\/pedi.13299"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12933-025-02637-4"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.2337\/dci23-0086"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.endien.2022.08.001"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13300-024-01572-z"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.3390\/bios12050288"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1111\/dom.14638"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13300-022-01240-0"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1111\/dme.14065"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13098-021-00714-8"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1080\/17843286.2018.1561780"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.2337\/dc23-1177"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.5114\/pedm.2023.130028"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.2337\/diacare.27.7.1761"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1177\/193229680900300305"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1177\/000456329903600408"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/bs.acc.2022.11.003"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.2337\/dc17-1669"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1177\/19322968221085273"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjdrc-2019-001045"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3465416.3483305"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejim.2025.05.017"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2025.0102"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.2337\/dci19-0028"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12933-020-01085-6"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2025.0092"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/S2213-8587(22)00319-9"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1101\/2025.01.01.25319870"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2005.7.253"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.2337\/diab.19.9.644"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.2337\/diacare.20.11.1655"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1089\/152091503322527021"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1177\/14791641211013734"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.3390\/s23167231"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdiacomp.2021.107950"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sleep.2018.03.013"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0139973"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.2337\/dc12-2711"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1177\/19322968241247219"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.4239\/wjd.v16.i3.99277"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1111\/dom.15068"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1111\/jdi.14153"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2023.0165"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2025.1563734"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1177\/1932296820905577"}],"container-title":["Journal of Diabetes Science and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/19322968261441312","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/19322968261441312","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/19322968261441312","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T07:16:46Z","timestamp":1778570206000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/19322968261441312"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,8]]},"references-count":53,"alternative-id":["10.1177\/19322968261441312"],"URL":"https:\/\/doi.org\/10.1177\/19322968261441312","relation":{},"ISSN":["1932-2968","1932-2968"],"issn-type":[{"value":"1932-2968","type":"print"},{"value":"1932-2968","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,8]]},"article-number":"19322968261441312"}}