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Because HRV declines by approximately 3\u20135% per decade due to age-related autonomic degeneration, absolute HRV values conflate the effects of aging with diabetes-specific autonomic dysfunction. We hypothesised that normalising HRV features using an age-dependent scaling factor would isolate the diabetes-specific component and improve glycemic status estimation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We analysed ECG-derived features from 43 male type 2 diabetes patients with strictly separated glycated hemoglobin (HbA1c;\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200929; 3-month glycemic average) and fasting blood glucose (FBG;\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200938; acute status). Leave-one-subject-out (LOSO) cross-validation (CV) with within-fold feature selection and standardisation prevented information leakage. Twenty machine learning algorithms and six age-adjustment methods were compared, with normalisation sensitivity tested across 20 parameter combinations. Statistical validation employed permutation testing (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009500) and bootstrap 95% confidence intervals.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Extra trees regression achieved the best performance: R\u00b2 = 0.222 (\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.476,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.009) for HbA1c and R\u00b2 = 0.086 (\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.344,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.034) for FBG, corresponding to mean absolute errors of 1.18% points and 2.27 mmol\/L respectively. Permutation testing confirmed that both associations exceeded the chance level (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.002). Contrary to our hypothesis, none of the six age-adjustment methods nor any of the 20 sensitivity parameter combinations improved performance, indicating that age-related HRV decline did not confound glycemic estimation in this cohort. CV hygiene differentially affected model families: tree-based ensembles maintained positive performance, whereas linear models collapsed to negative R\u00b2 values, revealing substantial bias from conventional practices. Neural networks with minimally configured hyperparameters failed for these sample sizes (R\u00b2 ranging from \u2212\u20098.2 to \u2212\u200910,879).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Strict within-fold preprocessing fundamentally alters conclusions in HRV-based glycemic status estimation, exposing inflated performance to conventional CV practices. Bootstrap confidence intervals excluding zero (HbA1c R\u00b2: [0.13, 0.82]; FBG R\u00b2: [0.10, 0.72]) provided statistical evidence for genuine HRV\u2013glycemic associations, but performance remained insufficient for standalone clinical use. This study establishes methodological standards for separating glycemic targets, subject-independent validation with within-fold preprocessing, and comprehensive baselines to advance non-invasive glycemic monitoring research.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical trial number<\/jats:title>\n                    <jats:p>Not Applicable.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-026-03455-8","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T11:58:19Z","timestamp":1775044699000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Re-evaluating heart rate variability biomarkers for glucose sensing: the impact of age normalisation and subject-independent validation"],"prefix":"10.1186","volume":"26","author":[{"given":"Md Basit","family":"Azam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarangthem Ibotombi","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"3455_CR1","unstructured":"International Diabetes Federation. 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