InsuSensCalc 0.1.0 (2026-06-09)

Formula corrections (change computed values)

Several index formulas were verified against their original publications and corrected. These fixes change the numeric output of the affected indices, so results from 0.1.0 are not directly comparable to 0.0.1 for these columns.

• VAI_Men_inv / VAI_Women_inv: fixed the denominator grouping so waist is divided by the full (constant + slope * BMI) term, and switched triglycerides and HDL-C to mmol/L as required by the original constants (Amato 2010, doi:10.2337/dc09-1825). Previous values were substantially off.
• LAP_Men_inv / LAP_Women_inv: triglycerides are now used in mmol/L (Kahn 2005, doi:10.1186/1471-2261-5-26); the previous mg/dL values were inflated by ~88.6x.
• McAuley_index: triglycerides are now used in mmol/L (McAuley 2001, doi:10.2337/diacare.24.3.460).
• Avignon_Sim: now computed as (0.137 * Si0 + Si120) / 2, the weighted mean defined by Avignon 1999 (doi:10.1038/sj.ijo.0800864). The previous expression returned an invalid, dataset-wide collapsed value.
• Matsuda_ISI: the mean glucose and insulin terms are now computed in the same converted units (mg/dL and µU/mL) as the fasting terms, fixing a unit mismatch (Matsuda & DeFronzo 1999, doi:10.2337/diacare.22.9.1462).
• Avignon_Sim: now uses the data-driven weighting w = mean(Si120) / mean(Si0), i.e. (w * Si0 + Si120) / 2, matching the source publication’s Table 2. Note this index therefore depends on the full sample.
• BigttSi: corrected the 30-minute insulin coefficient from 0.000565 to 0.000556 (Hansen 2007, doi:10.2337/dc06-1240).
• Homa_IR_inv: now uses glucose in mmol/L, giving the standard HOMA-IR (Matthews 1985, doi:10.1007/BF00280883); the previous mg/dL values were ~18x too large.

Maintenance

• Homa_IR_inv and HOMA_IR_rev_inv are both retained as separate columns; after the units fix they return the same standard HOMA-IR value (computed via mmol/L / 22.5 and mg/dL / 405 respectively).
• Removed library() calls from package source; imports are handled via NAMESPACE. Dropped the unused tidyr dependency.
• Documentation now states the correct triglyceride (* 88.57) and HDL-C (* 38.67) unit-conversion factors, and corrects a reversed insulin conversion comment.
• Added a citation for the package’s source publication (Suleman 2024, doi:10.1210/clinem/dgae275) to the DESCRIPTION field.

InsuSensCalc 0.0.1 (2024-04-02)

• Initial release of the InsuSensCalc, featuring the isi_calculator function.

New Features

• isi_calculator Function: A comprehensive tool for calculating surrogate insulin sensitivity indices based on various measurements, including fasting, Oral Glucose Tolerance Test (OGTT), and lipid (adipose) values. This function supports a wide range of indices calculations, making it a versatile tool for research in metabolic health and diabetes.

Capabilities

• Calculates indices using fasting glucose and insulin levels, including:
  • Fasting Insulin Sensitivity
  • HOMA-IR (and its inverse)
  • QUICKI
  • And several other fasting-related indices.
• Incorporates OGTT (0 min, 30 min, 120 min post-glucose load) values for:
  • Gutt Index
  • Matsuda Index
  • Insulin Sensitivity Index at 120 min
  • And more, adapting calculations based on available time points.
• Utilizes lipid (adipo) measurements like triglycerides, free fatty acids and HDL cholesterol for indices such as:
  • Visceral Adiposity Index (VAI) for Men and Women (inversed)
  • Lipid Accumulation Product (LAP)
  • ATIRI_inv
• Utilizes tracer and dxa based data measurements like rate of glycerol, palmitate and dxa based fat mass for indices such as:
  • LIRI_inv
  • Lipo_inv
  • TyG Index (inversed)
  • And other adipose-related indices.

Flexible Input

• The function accepts a dataframe containing the necessary variables for calculation and a character vector specifying the categories of indices to calculate ("fasting", "ogtt", "adipo"), allowing users to customize the scope of their analysis.

User-friendly

• Includes comprehensive documentation and examples to facilitate easy use and integration into research workflows.

This release lays the foundation for robust and flexible insulin sensitivity analysis within the R ecosystem, catering to a wide array of research needs in metabolic health.