Lipoprotein insulin resistance score and risk of incident diabetes during extended follow-up of 20 years: The Women's Health Study

  • Paulo H.N. Harada
    Affiliations
    Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA

    Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard Medical School, Boston, MA, USA

    Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA

    Center for Clinical and Epidemiological Research, Hospital Universitario at University of Sao Paulo, Sao Paulo, SP, Brazil
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  • Olga V. Demler
    Affiliations
    Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA

    Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard Medical School, Boston, MA, USA
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  • Sagar B. Dugani
    Affiliations
    Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA

    Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard Medical School, Boston, MA, USA

    Division of Internal Medicine, St. Michael's Hospital, University of Toronto, Toronto, Canada
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  • Akintunde O. Akinkuolie
    Affiliations
    Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA

    Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard Medical School, Boston, MA, USA
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  • Manickavasagar V. Moorthy
    Affiliations
    Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard Medical School, Boston, MA, USA
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  • Paul M. Ridker
    Affiliations
    Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard Medical School, Boston, MA, USA

    Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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  • Nancy R. Cook
    Affiliations
    Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard Medical School, Boston, MA, USA

    Harvard T. H. Chan School of Public Health, Boston, MA, USA
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  • Aruna D. Pradhan
    Affiliations
    Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard Medical School, Boston, MA, USA

    Veterans Affairs Boston Healthcare System, Boston, MA, USA
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  • Samia Mora
    Correspondence
    Corresponding author. Center for Lipid Metabolomics, Brigham and Women's Hospital, 900 Commonwealth Avenue East, Boston, MA 02215, USA.
    Affiliations
    Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA

    Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard Medical School, Boston, MA, USA

    Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Open AccessPublished:June 21, 2017DOI:https://doi.org/10.1016/j.jacl.2017.06.008

      Highlights

      • Lipoprotein insulin resistance (LPIR) score captures incipient effects of insulin resistance on lipoprotein metabolism.
      • LPIR was robustly associated with incident type II diabetes (T2D) along 20 years of follow-up.
      • The association persisted even in low T2D risk subgroups, as body mass index <25 kg/m2 and HbA1c <5.7%.
      • In the intermediate risk Framingham T2D score, LPIR improved risk classification in a clinically relevant magnitude.
      • LPIR may enhance T2D prevention strategies by detecting more accurately at-risk populations, who would otherwise be unaware of their increased risk.

      Background

      Type II diabetes (T2D) is preceded by prolonged insulin resistance and relative insulin deficiency incompletely captured by glucose metabolism parameters, high-density lipoprotein (HDL) cholesterol and triglycerides.

      Objective

      Whether lipoprotein insulin resistance (LPIR) score, a metabolomic marker, is associated with incident diabetes and improves risk reclassification over traditional markers on extended follow-up.

      Methods

      Among 25,925 nondiabetic women aged 45 years or older, LPIR was measured by nuclear magnetic resonance spectroscopy as a weighted score of very low density lipoprotein, low-density lipoprotein, and HDL particle sizes, and their subsets concentrations. We run adjusted cox regression models for LPIR with incident T2D (20.4 years median follow-up).

      Results

      Adjusting for demographics, body mass index, life style factors, blood pressure, and T2D family history, the LPIR hazard ratio for T2D (hazard ratio [HR] per standard deviation, 95% confidence interval) was 1.95 (1.85, 2.06). Further adjusting for HbA1c, C-reactive protein, triglycerides, HDL and low-density lipoprotein cholesterol, LPIR HR was attenuated to 1.41 (1.31, 1.53) and had the strongest association with T2D after HbA1C in mutually adjusted models. The association persisted even in those with optimal clinical profiles, adjusted HR per standard deviation 1.91 (1.17, 3.13). In participants deemed at intermediate T2D risk by the Framingham Offspring T2D score, LPIR led to a net reclassification of 0.145 (0.117, 0.175).

      Conclusion

      In middle-aged or older healthy women followed prospectively for over 20 years, LPIR was robustly associated with incident T2D, including among those with an optimal clinical metabolic profile. LPIR improved T2D risk classification and may guide early and targeted prevention strategies.

      Graphical abstract

      Keywords

      Introduction

      Type II diabetes (T2D) is a global epidemic with increasing prevalence worldwide.
      • Murray C.J.
      • Barber R.M.
      • Foreman K.J.
      • et al.
      DALYs GBD, Collaborators H
      Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990-2013: quantifying the epidemiological transition.
      Since T2D is preceded by prolonged preclinical insulin resistance and beta cell dysfunction,
      • Song Y.
      • Manson J.E.
      • Tinker L.
      • et al.
      Insulin sensitivity and insulin secretion determined by homeostasis model assessment and risk of diabetes in a multiethnic cohort of women: the Women's Health Initiative Observational Study.
      • Festa A.
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      • Wagenknecht L.E.
      • Haffner S.M.
      The natural course of beta-cell function in nondiabetic and diabetic individuals: the Insulin Resistance Atherosclerosis Study.
      biomarkers of these early processes could identify and guide timely interventions in individuals susceptible to T2D. Despite glucose metabolism measures being good risk predictors and the benchmark for T2D diagnosis, current dysglycemia parameters are insensitive to incipient insulin resistance.
      • Weyer C.
      • Bogardus C.
      • Mott D.M.
      • Pratley R.E.
      The natural history of insulin secretory dysfunction and insulin resistance in the pathogenesis of type 2 diabetes mellitus.
      Nondiabetic individuals have alterations in hepatic lipoprotein metabolism due to insulin resistance that take place when glucose levels are still normal.
      • Sorensen L.P.
      • Sondergaard E.
      • Nellemann B.
      • Christiansen J.S.
      • Gormsen L.C.
      • Nielsen S.
      Increased VLDL-triglyceride secretion precedes impaired control of endogenous glucose production in obese, normoglycemic men.
      As insulin resistance is associated with future T2D, myocardial infarction, and overall mortality in nondiabetic subjects,
      • Hedblad B.
      • Nilsson P.
      • Engstrom G.
      • Berglund G.
      • Janzon L.
      Insulin resistance in non-diabetic subjects is associated with increased incidence of myocardial infarction and death.
      • Ausk K.J.
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      • Ioannou G.N.
      Insulin resistance predicts mortality in nondiabetic individuals in the U.S.
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      • Shaper A.G.
      • Alberti K.G.
      Serum true insulin concentration and the risk of clinical non-insulin dependent diabetes during long-term follow-up.
      even earlier identification of such a process is of utmost importance.
      Conventional lipoprotein metabolism biomarkers, such as high-density lipoprotein cholesterol (HDL-C) and triglycerides correlate with insulin resistance and incident T2D.
      • Wilson P.W.
      • Meigs J.B.
      • Sullivan L.
      • et al.
      Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study.
      • Wu L.
      • Parhofer K.G.
      Diabetic dyslipidemia.
      However, they do not reflect detailed insulin-resistant dyslipoproteinemia. This is characterized by hypersecretion of triglyceride-rich very low-density lipoprotein particles (VLDL-P) followed by concerted actions of lipases and transferases, which leads to accumulation of small dense low-density lipoprotein particles (LDL-P) and reduction in large HDL particles.
      • Adiels M.
      • Olofsson S.O.
      • Taskinen M.R.
      • Boren J.
      Overproduction of very low-density lipoproteins is the hallmark of the dyslipidemia in the metabolic syndrome.
      Despite that each of these lipoproteins has been associated with insulin resistance and incident T2D,
      • Mackey R.H.
      • Mora S.
      • Bertoni A.G.
      • et al.
      Lipoprotein particles and incident type 2 diabetes in the multi-ethnic study of atherosclerosis.
      • Mora S.
      • Otvos J.D.
      • Rosenson R.S.
      • Pradhan A.
      • Buring J.E.
      • Ridker P.M.
      Lipoprotein particle size and concentration by nuclear magnetic resonance and incident type 2 diabetes in women.
      • Festa A.
      • Williams K.
      • Hanley A.J.
      • et al.
      Nuclear magnetic resonance lipoprotein abnormalities in prediabetic subjects in the Insulin Resistance Atherosclerosis Study.
      • Garvey W.T.
      • Kwon S.
      • Zheng D.
      • et al.
      Effects of insulin resistance and type 2 diabetes on lipoprotein subclass particle size and concentration determined by nuclear magnetic resonance.
      • Goff Jr., D.C.
      • D'Agostino Jr., R.B.
      • Haffner S.M.
      • Otvos J.D.
      Insulin resistance and adiposity influence lipoprotein size and subclass concentrations. Results from the Insulin Resistance Atherosclerosis Study.
      single lipid or lipoprotein parameters may not reflect insulin-resistant dyslipoproteinemia overall.
      Lipoprotein insulin resistance (LPIR) score is a novel composite metabolomic biomarker that captures the multidimensional effects of insulin resistance on the lipoprotein metabolic chain.
      • Shalaurova I.
      • Connelly M.A.
      • Garvey W.T.
      • Otvos J.D.
      Lipoprotein insulin resistance index: a lipoprotein particle-derived measure of insulin resistance.
      LPIR is a clinically available test measured by a targeted metabolomics approach using high throughput nuclear magnetic resonance spectroscopy. It is a weighted score of VLDL, LDL, and HDL particle sizes, and large VLDL, small LDL, and large HDL particle concentrations that are more strongly related to insulin resistance than each of its individual subclasses.
      • Shalaurova I.
      • Connelly M.A.
      • Garvey W.T.
      • Otvos J.D.
      Lipoprotein insulin resistance index: a lipoprotein particle-derived measure of insulin resistance.
      Recently, LPIR was associated with incident T2D in a prospective study,
      • Mackey R.H.
      • Mora S.
      • Bertoni A.G.
      • et al.
      Lipoprotein particles and incident type 2 diabetes in the multi-ethnic study of atherosclerosis.
      even among individuals treated with high-intensity statin.
      • Dugani S.B.
      • Akinkuolie A.O.
      • Paynter N.
      • Glynn R.J.
      • Ridker P.M.
      • Mora S.
      Association of lipoproteins, insulin resistance, and rosuvastatin with incident type 2 diabetes mellitus : secondary analysis of a randomized clinical trial.
      We hypothesized that LPIR may identify T2D risk years before dysglycemia onset and other metabolic derangements.
      We addressed the association of baseline LPIR with incident T2D in a cohort of nondiabetic healthy middle-aged women at baseline (N = 25,925) followed prospectively for more than 20 years. We also examined if LPIR can enhance risk prediction over a composite clinical score for incident T2D.

      Material and methods

       Population study

      We studied the Women's Health Study (WHS), a completed double blinded, placebo-controlled trial of low-dose aspirin and vitamin E on the prevention of primary cardiovascular events and cancer in apparently healthy female healthcare professionals aged ≥45 years.
      • Ridker P.M.
      • Cook N.R.
      • Lee I.M.
      • et al.
      A randomized trial of low-dose aspirin in the primary prevention of cardiovascular disease in women.
      Individuals were enrolled between 1992 and 1995 and followed prospectively through 2015. All participants signed written informed consent, approved by the Institutional Review Board of Brigham and Women's Hospital (Boston, MA). At enrollment, participants answered questionnaires for demographics, anthropometrics, medical history, and lifestyle behaviors. From 39,876 individuals, 28,345 consented to have a blood sample stored in liquid nitrogen. We excluded participants with baseline T2D diagnosis, glycated hemoglobin (HbA1c) ≥6.5%, those using lipid-lowering therapy, and those with no LPIR measurement, resulting in 25,925 individuals (Fig. 1).
      Figure thumbnail gr1
      Figure 1Study population selection flow chart.

       Population characteristics

      At study entry age, race, smoking, alcohol intake, physical activity, menopausal status, postmenopausal hormone use, and family history of diabetes were self-reported on questionnaires.
      • Mora S.
      • Otvos J.D.
      • Rosenson R.S.
      • Pradhan A.
      • Buring J.E.
      • Ridker P.M.
      Lipoprotein particle size and concentration by nuclear magnetic resonance and incident type 2 diabetes in women.
      Body mass index (BMI) was measured by weight (kilograms) divided by height (meters) squared.

       Laboratory parameters

      Baseline blood was collected in ethylenediaminetetraacetic acid tubes and stored in liquid nitrogen (−170°C) until the laboratory analysis. Standard lipids were measured by direct assays (Roche Diagnostics, Indianapolis, IN), high-sensitivity C-reactive protein (hs-CRP) by a validated assay (Denka Seiken, Niigata, Japan), and HbA1c by turbidimetric immunoinhibition using hemolyzed whole blood or packed red cells (Roche Diagnostics).
      • Mora S.
      • Otvos J.D.
      • Rosenson R.S.
      • Pradhan A.
      • Buring J.E.
      • Ridker P.M.
      Lipoprotein particle size and concentration by nuclear magnetic resonance and incident type 2 diabetes in women.
      Fasting plasma glucose was not measured in WHS.
      Targeted metabolomics approach (Liposcience, Inc, now LabCorp, Raleigh, NC) was used to detect proton nuclear magnetic resonance spectroscopy methyl group signal from lipoprotein subclasses: large, medium, and small VLDL; large, medium, and small HDL; and large and small LDL concentrations.
      • Shalaurova I.
      • Connelly M.A.
      • Garvey W.T.
      • Otvos J.D.
      Lipoprotein insulin resistance index: a lipoprotein particle-derived measure of insulin resistance.
      Mean VLDL, LDL, and HDL particle sizes derive from weighted averages of each subclass diameter relative to its mass percentage. LPIR is a composite weighted score of 6 lipoprotein parameters with Homeostatic Model Assessment–Insulin Resistance: VLDL, LDL, and HDL average particle size; and concentrations of large VLDL, small LDL, and large HDL particles, previously described.
      • Shalaurova I.
      • Connelly M.A.
      • Garvey W.T.
      • Otvos J.D.
      Lipoprotein insulin resistance index: a lipoprotein particle-derived measure of insulin resistance.
      LPIR interassay repeatability from 80 replicate analyses of 8 plasma pools over 20 days had a coefficient of variation of 6% within-run and 9% between-run.
      • Shalaurova I.
      • Connelly M.A.
      • Garvey W.T.
      • Otvos J.D.
      Lipoprotein insulin resistance index: a lipoprotein particle-derived measure of insulin resistance.
      Each parameter value corresponds to a point score from zero to a capped value, and their sum from 0 to 100, with increasing scores signaling more insulin resistance (Table 1).
      Table 1LPIR score calculation algorithm
      Adapted from reference.
      • Shalaurova I.
      • Connelly M.A.
      • Garvey W.T.
      • Otvos J.D.
      Lipoprotein insulin resistance index: a lipoprotein particle-derived measure of insulin resistance.
      VLDL size (nm)VLDL size scoreLarge VLDL-P (nmol/L)Large VLDL-P scoreLDL size (nm)LDL size scoreSmall LDL-P (nmol/L)Small LDL-P scoreHDL size (nm)HDL size scoreLarge HDL-P (μmol/L)Large HDL-P score
      <39.20<0.70<21.06<900<8.720<3.112
      39.2–41.110.7–1.0221.0590–10418.7163.1–4.010
      41.2–42.821.1–1.3521.13105–12838.8124.1–5.49
      42.8–44.341.4–1.5721.22129–37248.9105.5–6.38
      44.4–46.061.6–1.79>21.20373–9616996.4–7.16
      46.1–48.191.8–2.512>96189.1–9.277.2–8.04
      48.2–50.3102.6–3.7159.358.1–9.32
      50.4–51.6113.8–5.3189.4–9.54>9.30
      51.7–53.2125.4–7.9199.6–9.72
      53.3–55.315>7.922>9.70
      55.4–58.418
      58.5–61.019
      61.1–63.022
      63.1–64.125
      64.2–65.128
      >65.132
      LDL-P, low-density lipoprotein particles; LPIR, lipoprotein insulin resistance; HDL-P, high-density lipoprotein particles; VLDL-P, very low-density lipoprotein particles.
      LPIR interassay repeatability from 80 replicate analyses of 8 plasma pools over 20 days had a coefficient of variation of 6% within-run and 9% between-run.
      • Shalaurova I.
      • Connelly M.A.
      • Garvey W.T.
      • Otvos J.D.
      Lipoprotein insulin resistance index: a lipoprotein particle-derived measure of insulin resistance.

       Incident T2D ascertainment

      Cases of incident T2D were initially identified by self-report on annual questionnaires that asked if and when the participant had been diagnosed with T2D since baseline or the previous questionnaire.
      • Pradhan A.D.
      • Cook N.R.
      • Manson J.E.
      • Ridker P.M.
      • Buring J.E.
      A randomized trial of low-dose aspirin in the prevention of clinical type 2 diabetes in women.
      • Liu S.
      • Lee I.M.
      • Song Y.
      • et al.
      Vitamin E and risk of type 2 diabetes in the women's health study randomized controlled trial.
      Self-reported cases were then confirmed by physician-administered telephone interviews
      • Ding E.L.
      • Song Y.
      • Manson J.E.
      • Pradhan A.D.
      • Buring J.E.
      • Liu S.
      Accuracy of administrative coding for type 2 diabetes in children, adolescents, and young adults.
      or a self-administered supplemental questionnaire, using the American Diabetes Association diagnostic criteria as previously described.
      • Liu S.
      • Lee I.M.
      • Song Y.
      • et al.
      Vitamin E and risk of type 2 diabetes in the women's health study randomized controlled trial.
      In a validation study, both interview-based and supplemental questionnaire-based confirmation-yielded positive predictive values >90% in comparison to medical record review.
      • Ding E.L.
      • Song Y.
      • Manson J.E.
      • Pradhan A.D.
      • Buring J.E.
      • Liu S.
      Accuracy of administrative coding for type 2 diabetes in children, adolescents, and young adults.
      In particular, the positive predictive value of the supplemental questionnaire compared with medical record review was 99% (95% confidence interval [CI] 97%–100%). Overall, in 95% of all self-reported T2D events, sufficient information for confirmation or disconfirmation of the endpoint was obtained, and only confirmed cases were used in this analysis. The American Diabetes Association diagnostic criteria
      Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus.
      required at least 1 of the following: (1) presence of more than 1 classic symptom of hyperglycemia (ie, polyuria, polydipsia, weight loss, with or without polyphagia, and blurred vision) plus either a fasting plasma glucose of 126 mg/dL (7.0 mmol/L) or higher or random plasma glucose 200 mg/dL (11.1 mmol/L) or higher; (2) in the absence of symptoms, 2 or more elevated plasma glucose concentrations (fasting plasma glucose ≥126md/dL [7.0 mmol/L], random plasma glucose ≥200 mg/dL [11.1 mmol/L], or 2-hour plasma glucose ≥200 mg/dL [11.1 mmol/L] during oral glucose tolerance testing); or (3) use if insulin or an oral hypoglycemic agent.

       Statistical analysis

      LPIR values were described by percentages and medians/interquartiles. Trend across quartiles were tested by Cochran–Mantel–Haenszel for categorical variables or Jonckheere-Terpstra for continuous. The Spearman correlation of LPIR with other T2D predictors was tested.
      Cumulative incidence curves and the log-rank test were computed for LPIR and HbA1c quartiles. Cox proportional hazards models were used to estimate hazard ratios (HRs) for incident T2D across LPIR quartiles and per standard deviation.
      • Murray C.J.
      • Barber R.M.
      • Foreman K.J.
      • et al.
      DALYs GBD, Collaborators H
      Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990-2013: quantifying the epidemiological transition.
      Trend across LPIR quartiles was assessed using median levels. Model adjustment was incremental: Model 1: age, race, BMI, current smoking, physical activity, education, menopause, hormone replacement therapy, systolic blood pressure, antihypertensive treatment, and family history of T2D; Model 2: Model 1 plus HbA1c; Model 3: Model 2 plus log hs-CRP; and Model 4: Model 3 plus HDL, LDL-C, and log triglycerides. Testing for interaction with log time found a lack of proportionality across the full follow-up, but hazards were proportional on separate analysis for the first and last 10 years. As estimators for these 2 time intervals and the full follow-up were similar, we considered full follow-up most informative. Cumulative incidence of T2D by LPIR quartiles was plotted for the first and last 10 years.
      Other T2D clinical predictor's standard deviation (SD) HRs (95% CIs) obtained from model 4 were plotted. We also tested the HR for LPIR and T2D by clinically defined strata: age (<55or ≥55 years); BMI (<25 or ≥25 kg/m2); family history of T2D; HbA1c (<5.7% or ≥5.7%); HDL-C (<50% or ≥50 mg/dL); triglycerides (<150 or ≥150 mg/dL) and hs-CRP (<2 or ≥2 mg/L); and metabolically healthy profile (all the following BMI <25 kg/m2, HDL ≥50 mg/dL, triglycerides <150 mg/dL, HbA1< 5.7%, hs-CRP <2 mg/L, blood pressure <140/90 mm Hg, no antihypertensive treatment and no family history of T2D), or not metabolically healthy (at least 1 of those characteristics absent). Effect modification was tested by an interaction term between LPIR and each stratum for T2D association. We also assessed T2D risk by LPIR and HbA1c composite groups (HbA1c < 5.7 or 5.7%–6.4% and LPIR <48 or ≥48, the median LPIR value) by cumulative incidence curves, log-rank test and Cox models.
      Discrimination and reclassification of LPIR over the Framingham Offspring (FOS) T2D score
      • Wilson P.W.
      • Meigs J.B.
      • Sullivan L.
      • et al.
      Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study.
      was examined at 10 and 16 years. We chose 16 years, instead of 20 years, as many participants were censored or had an event at 20 years, which affects model precision. The FOS score is calculated by integrating the weighted values of age, family history of T2D, BMI, systolic blood pressure, antihypertensive medication, HDL-C, triglycerides, and fasting plasma glucose, which results in an expected T2D percentage incidence in a determined time window (8 years in the original article). As fasting plasma glucose was unavailable, we used HbA1c instead. The original score predictors were fitted to our population for balanced comparison. Discrimination performance was tested by Harrell's c-index
      • Harrell Jr., F.E.
      • Lee K.L.
      • Califf R.M.
      • Pryor D.B.
      • Rosati R.A.
      Regression modelling strategies for improved prognostic prediction.
      and the difference of LPIR over FOS was tested by 10-fold cross-validation and the 95% CI by 1000 bootstrap replications, which avoid overoptimism. Calibration was tested by the Greenwood–Nam–D'Agostino for survival method
      • Demler O.V.
      • Paynter N.P.
      • Cook N.R.
      Tests of calibration and goodness-of-fit in the survival setting.
      by predicted probabilities deciles and displayed in calibration plots. Discrimination and reclassification of LPIR over FOS was tested by the integrated discrimination improvement (IDI; absolute and relative) and categorical net reclassification index (NRI).
      • Pencina M.J.
      • D'Agostino Sr., R.B.
      • D'Agostino Jr., R.B.
      • Vasan R.S.
      Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond.
      For NRI, we considered the original FOS study limits for low, intermediate, and high T2D risk.
      • Wilson P.W.
      • Meigs J.B.
      • Sullivan L.
      • et al.
      Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study.
      Assuming linear events accumulation, the original 3% and 10% limits at 8 years, correspond to 3.75% and 12.5% at 10 years; and 6% and 20% at 16 years.

      Results

       Baseline population characteristics

      Overall, our study population was generally healthy: age, median 52.7 years (25th to 75th percentile: 48.9–58.6); BMI, median 24.8 kg/m2 (25th to 75th percentile: 22.3–28.2); and median HbA1c 5.0% (25th to 75th percentile: 4.8–5.2%). Nearly one-quarter had metabolic syndrome or T2D family history. The median LPIR was 48 (25th to 75th percentile: 29–67). There were 2259 T2D cases over 463,717 person-years (0.49 cases per 100 person-years) along 20.4 years median follow-up (maximum 21.6 years). For increasing LPIR quartiles, characteristics tracked with T2D risk factors (Table 2): older age, higher BMI, and more prevalent physical inactivity and T2D family history. Likewise, metabolic syndrome characteristics after increasing LPIR quartiles: blood pressure, HDL-C, triglycerides, HbA1c, and hs-CRP.
      Table 2Baseline characteristics according to LPIR quartiles
      CharacteristicsLPIR quartile (score range)
      Q1 (<30)Q2 (30–47)Q3 (48–67)Q4 (>67)
      Age (y)51.9 (48.5–57.3)52.6 (48.8–58.4)53.2 (49.1–59.2)53.4 (49.2–59.4)
      Caucasian, %95.195.895.395.9
      BMI (kg/m2)22.8 (21.2–24.9)23.9 (21.9–26.6)25.6 (23.0–28.8)27.8 (24.8–31.5)
      Current smoking, %9.011.412.813.4
      Physical activity (≥1/wk), %50.545.840.836.7
      Alcohol (1 ≥ drink/d), %12.112.210.37.4
      Education (≥BS), %51.945.642.738.1
      Family history T2D, %21.022.026.029.3
      Metabolic syndrome, %1.16.826.465.8
      Systolic blood pressure (mm Hg)115 (115–125)115 (115–125)125 (115–135)125 (115–135)
      Hypertension treatment, %6.18.713.319.4
      Postmenopausal, %48.752.855.956.5
      Hormone therapy, %40.544.944.441.7
      Laboratory measurements
       Total cholesterol (mg/dL)202 (179–228)203 (179–228)209 (185–235)217 (192–245)
       HDL-C (mg/dL)65.1 (56.9–74.7)55.8 (48.5–64.3)48.6 (42.4–55.9)41.8 (36.4–48.2)
       LDL-C (mg/dL)113 (94–134)118 (98–140)125 (104–148)129 (108–152)
       Triglycerides (mg/dL)74 (58–93)98 (78–127)133 (106–170)199 (158–261)
       Apolipoprotein B (mg/dL)86.3 (73.7–102.8)93.8 (79.9–111.4)106.4 (88.9–123.6)118.3 (99.3–136.3)
       Apolipoprotein A1 (mg/dL)161 (146–179)153 (137–172)145 (130–164)137 (124–154)
       hs-CRP (mg/L)1.0 (0.4–2.3)1.5 (0.7–3.4)2.4 (1.1–4.7)3.3 (1.7–6.1)
       HbA1c (%)4.9 (4.8–5.1)5.0 (4.8–5.1)5.0 (4.8–5.2)5.1 (4.9–5.3)
      BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein; LDL-C, low-density lipoprotein cholesterol; LPIR, lipoprotein insulin resistance.
      Continuous variables displayed by median (25th–75th percentiles) and categorical ones by number of observations (percentage). Increasing quartiles trend test by Cochran–Mantel–Haenszel for categorical variables and Jonckheere Terpstra for continuous ones. All P values < .001, except for hormone therapy (P value = .240).

       LPIR correlations

      Comparison with other T2D clinical predictors, LPIR was weakly correlated with HbA1c (ρ 0.18, P < .001; Supplementary Table 1). As expected, LPIR strongly correlated with HDL-C and triglycerides (respectively, ρ 0.66 and 0.75, P < .001), and its individual lipoprotein components.
      In unadjusted analysis, increasing LPIR quartiles corresponded to higher T2D incidence (log-rank P < .001; Fig. 2A), strikingly similar to HbA1c quartiles curves (log-rank P < .001; Fig. 2B).
      Figure thumbnail gr2
      Figure 2Diabetes cumulative incidence. (A) LPIR quartiles. LPIR score across quartiles Q1 (<30), Q2 (30–47), Q3 (48–67), and Q4 (>67). (B) HbA1c quartiles. HbA1c values (%) across quartiles Q1 (<4.83), Q2 (4.83–4.99), Q3 (4.99–5.17), and Q4 (>5.17).

       LPIR and incident T2D

      The LPIR risks for the first and last 10 years of follow-up were similar to the full follow-up (Supplementary Table 2 and Supplementary Fig. 1).
      In adjusted analysis, increasing LPIR quartiles were associated with T2D risk in a linear fashion (Table 3). First, adjusted for age, race, smoking, physical activity, education, menopause status, hormone replacement therapy, blood pressure, antihypertensive treatment, BMI, and family history of T2D; HRs (95% CIs) for LPIR Q2 to Q4 vs Q1 were respectively: 1.67 (1.34, 2.07); 3.23 (2.66, 3.92); and 5.68 (4.70, 6.87). After incremental adjustment for HbA1c (Model 2), and also hs-CRP (Model 3), the estimators were mildly attenuated (model 3 Q4 vs Q1 HR: 4.53 [95% CI: 3.74, 5.49]). Further adjusting for lipids (model 4), the HR of Q4 vs Q1 was attenuated to 2.21 (95% CI: 1.75, 2.80). Alternatively, in model 4 for each LPIR SD increment (23.4 units), T2D risk was 41% higher (95% CI: 31%, 53%). Additional adjustment for alcohol consumption and fasting state at blood draw did not change the results (data not shown).
      Table 3LPIR and incident diabetes–full follow-up
      LPIR quartilesQ1Q2Q3Q4P value for linear trendPer standard deviationP value
      Events/N (%)131/6546 (2.00%)247/6386 (3.87%)611/6571 (9.30%)1270/6422 (19.78%)
      T2D/100 person-years (95% CI)0.11 (0.09–0.13)0.21 (0.19–0.24)0.52 (0.49–0.57)1.21 (1.15–1.27)
      Unadjusted1.00 (ref.)1.97 (1.59, 2.43)4.94 (4.09, 5.97)11.5 (9.61, 13.77)<.00012.57 (2.45, 2.70)<.0001
      M1 HR (95% CI)1.00 (ref.)1.67 (1.34, 2.07)3.23 (2.66, 3.92)5.68 (4.70, 6.87)<.00011.95 (1.85, 2.06)<.0001
      M2 HR (95% CI)1.00 (ref.)1.72 (1.38, 2.13)3.15 (2.59, 3.83)5.03 (4.16, 6.09)<.00011.81 (1.72, 1.91)<.0001
      M3 HR (95% CI)1.00 (ref.)1.64 (1.32, 2.04)2.89 (2.37, 3.51)4.53 (3.74, 5.49)<.00011.75 (1.67, 1.85)<.0001
      M4 HR (95% CI)1.00 (ref.)1.26 (1.01, 1.57)1.75 (1.41, 2.17)2.21 (1.75, 2.80)<.00011.41 (1.31, 1.53)<.0001
      CI, confidence interval; HR, hazard ratio; LPIR, lipoprotein insulin resistance; T2D, type II diabetes.
      Adjusted HRs for LPIR quartiles and LPIR per standard deviation increment (23.4 score units). P values for trend according to quartiles median values.
      Model 1 (M1): age, race, smoking status, physical activity, education, menopause status, hormone replacement therapy, systolic blood pressure, antihypertensive treatment, body mass index, family history of T2D.
      Model 2 (M2): M1 plus HbA1c.
      Model 3 (M3): M2 plus hs-C-reactive protein logarithm.
      Model 4 (M4): M3 plus lipid parameters not contemplated by LPIR (HDL-C, LDL-C, and triglycerides logarithm).

       Comparative strengths for incident T2D

      Comparing the LPIR T2D risk with other traditional predictors in a mutually adjusted model (model 4), LPIR outranked triglycerides, BMI, hs-CRP, and had at least similar magnitude to lower HDL-C, 1.35 per SD (95% CI: 1.26, 1.45). LPIR SD HR was only lower than HbA1c, 1.73 (95% CI: 1.68, 1.79; Fig. 3).
      Figure thumbnail gr3
      Figure 3Standardized hazard ratios for predictors of diabetes. Mutually adjusted covariates plus those from model 4 in main analysis.

       Subgroups analysis

      In stratified analysis, LPIR was associated with risk of T2D incidence in all subgroups (Fig. 4). The HRs ranged from 1.19 (95% CI: 1.01, 1.40) for HbA1c ≥ 5.7% to 1.91% (95% CI: 1.17, 3.13) in metabolically healthy stratum. Overall, LPIR relative risks were higher in individuals at lower T2D risk by clinical factors. There was significant effect modification for BMI, family history, HbA1c, and hs-CRP strata.
      Figure thumbnail gr4
      Figure 4LPIR standardized hazard ratios within each stratum. Model adjustment for age, race, smoking status, physical activity, education, menopause status, hormone replacement therapy, systolic blood pressure, antihypertensive treatment, body mass index (BMI), family history of diabetes, HbA1c, log hs-CRP, HDL-C, LDL-C, and log triglycerides (Trigl.). Metabolically healthy defined as those with BMI <25 kg/m2 and HDL ≥50 mg/dL and triglycerides <150 mg/dL and HbA1< 5.7% and hs-CRP <2 mg/L and blood pressure <140/90 mm Hg and no antihypertensive treatment and no family history of T2D; or not if otherwise.
      For groups defined by prediabetic state presence (HbA1c <5.7% or 5.7%–6.5%) and LPIR (<48or ≥48, study median), there was incremental adjusted risk from no prediabetes and low LPIR group (reference) to no prediabetes and high LPIR, to prediabetes and low LPIR, to prediabetes and high LPIR (HR 6.8, 95% CI: 5.5, 8.3 vs the reference; Fig. 5).
      Figure thumbnail gr5
      Figure 5Groups according to prediabetes (PD) status (HbA1c <5.7% or ≥5.7%) and LPIR <48 or ≥48. PD/H LPIR (prediabetes and LPIR ≥ 48), PD/L LPIR (prediabetes and LPIR < 48); NPD/H LPIR (no prediabetes and LPIR ≥ 48), and NPD/L LPIR (no prediabetes and LPIR < 48). Adjusted analysis covariates: age, race, smoking status, physical activity, education, menopause status, hormone replacement therapy, systolic blood pressure, antihypertensive treatment, body mass index, family history of diabetes, HbA1c, hs-CRP, HDL-C, LDL-C, and triglycerides. P for trend <.001 across quartiles in unadjusted and adjusted analysis. Unadjusted HR (95% CI) for PD/LPIR groups vs the NPD/L LPIR (reference): NPD/H LPIR 5.4 (4.7, 5.9); PD/L LPIR 10.1 (7.0, 14.6); and PD/H LPIR 40.7 (34.4, 48.1).

       Model performance with LPIR compared with a clinical T2D risk score

      At 16 years, the c-index for the FOS model was 0.850 (95% CI: 0.843, 0.857) and improved by 0.002 (95% CI: 0.001, 0.004) with LPIR (Supplementary Table 3). At 16 years, LPIR improved overall model performance (goodness of fit likelihood ratio χ2 87.49), model discrimination (IDI 0.009 [95% CI: 0.006, 0.011]; relative IDI 0.043 [95% CI: 0.031, 0.056]) and reclassification (NRI 0.028 [95% CI: 0.015, 0.043]). Regarding FOS, LPIR only and FOS + LPIR models calibration, they generally overestimated T2D risk in the low and moderate range but underestimated in the higher risk range (Fig. 6A–F). Interestingly, just the LPIR only model at 16 years was statistically calibrated (Greenwood-Nam-D'Agostino test P value .187; Fig. 6E). In participants deemed at intermediate T2D risk by the FOS model, LPIR led to a correct reclassification in 5.7% for events, 7.6% for nonevents and net reclassification of 0.145 (0.117, 0.175; Table 4). Overall, similar results were obtained at 10 years.
      Figure thumbnail gr6
      Figure 6Calibration plots for type diabetes predictive models. (A) Calibration plot Framingham Offspring T2D at 10 years. (B) Calibration plot LPIR only at 10 years. (C) Calibration plot Framingham Offspring T2D + LPIR at 10 years. (D) calibration plot Framingham Offspring T2D at 16 years. (E) Calibration plot LPIR only at 16 years. (F) calibration plot Framingham Offspring T2D + LPIR at 16 years. Variables in the Framingham Offspring model, age, family history of T2D, body mass index, systolic blood pressure, antihypertensive medication, HDL, triglycerides, and HbA1c. For calibration analysis, Greenwood–Nam–D'Agostino test was applied, where a P value < .05 indicate a noncalibrated model. Tables below graphs displaying the predicted risk by corresponding model, the observed risk, and the absolute difference of the former minus the last. Red color indicates overestimation and blue one underestimation of the prediction model.
      Table 4Net reclassification improvement for incident type II diabetes (T2D) with addition of LPIR to Framingham Offspring T2D in intermediate risk women
      Years of follow-up events or non-events NRITotal reclassified %LowIntermediateHighCorrectly reclassified %Overall NRI
      10 y
       Events13.2%47783722.8%0.126 (0.087, 0.168)
       Nonevents15.8%56838751588.9%
      16 y
       Events12.4%29759785.7%0.145 (0.117, 0.175)
       Nonevents15.6%69150232377.6%
      NRI, net reclassification improvement.
      Variables in the Framingham Offspring model, age, family history of T2D, body mass index, systolic blood pressure, antihypertensive medication, HDL, triglycerides, and HbA1c. NRI risk limits for low, intermediate, and high categories were 3.75% and 12.5% for 10 years and 6% and 20% for 16 years. NRI estimator and 95% CI were calculated by 1000 bootstrap replications, which explains the small divergence vs the sum of % net reclassification of events and nonevents.

      Discussion

      LPIR, a novel metabolomic biomarker of lipoprotein insulin resistance, is associated with incident T2D in a population of otherwise healthy women followed prospectively for more than 20 years. This association persisted after adjustment for a wide range of potential confounders and was observed even in those at very low risk for T2D based on the standard clinical profile. Furthermore, LPIR improved T2D risk assessment beyond traditional markers during a very long follow-up. Thus, the LPIR metabolic signature detects T2D risk earlier and largely independent from traditional markers, including HbA1c.
      In our study, LPIR correlated with demographic and laboratory markers of T2D risk, but contributed to T2D risk largely independently of all these markers. Moreover, this association persisted even in subgroups at low short-term risk for T2D. Thus, LPIR captures information on very early insulin-resistant dyslipoproteinemia
      • Lewis G.F.
      • Uffelman K.D.
      • Szeto L.W.
      • Steiner G.
      Effects of acute hyperinsulinemia on VLDL triglyceride and VLDL apoB production in normal weight and obese individuals.
      • Malmstrom R.
      • Packard C.J.
      • Caslake M.
      • et al.
      Effects of insulin and acipimox on VLDL1 and VLDL2 apolipoprotein B production in normal subjects.
      signaling T2D risk. Detecting T2D susceptibility above and beyond current prediction models opens up a novel and earlier dimension to T2D prevention. On the other hand, among participants with more advanced dysglycemia (ie, baseline HbA1c 5.7%–6.5%), the smaller magnitude (on a relative scale compared with participants with HbA1c <5.7%) of LPIR-related risk also translated into clinically relevant incremental absolute risk differences as these participants had high incidence rates (Fig. 5). It is yet to be determined whether LPIR is directly involved in T2D pathophysiology or is just a marker of underlying mechanisms.
      Our results reproduce and expand Mackey et al findings
      • Mackey R.H.
      • Mora S.
      • Bertoni A.G.
      • et al.
      Lipoprotein particles and incident type 2 diabetes in the multi-ethnic study of atherosclerosis.
      in the Multi-Ethnic Study of Atherosclerosis (MESA) population. In that population of 5314 men and women aged 45 to 84 years followed for 7.7 years, LPIR HR for T2D was 1.59 for Q4 vs Q1 in a model adjusted for fasting glucose, triglycerides to HDL-C ratio, and other traditional markers. In our female population, there was higher risk for Q4 vs Q1, fully adjusted HR 2.21 (95% CI: 1.75, 2.80), which may be partly explained by a different population profile, model adjustment, or follow-up time. Similar to our study, in the MESA population, LPIR was associated with T2D risk even in those with low T2D risk. Moreover, in MESA, LPIR was associated with T2D largely independent of glucose, insulin, and Homeostasis Model Assessment–Insulin Resistance, which reinforces the LPIR unique risk information. Hence, LPIR detects T2D susceptibility years before clinically detectable glycemic abnormalities. Besides that, LPIR was also associated with short-term incident T2D, even among those under statin therapy initiation in the higher T2D risk JUPITER trial population.
      • Dugani S.B.
      • Akinkuolie A.O.
      • Paynter N.
      • Glynn R.J.
      • Ridker P.M.
      • Mora S.
      Association of lipoproteins, insulin resistance, and rosuvastatin with incident type 2 diabetes mellitus : secondary analysis of a randomized clinical trial.
      The overall effects of insulin resistance on lipoprotein metabolism are well established,
      • Garg A.
      • Helderman J.H.
      • Koffler M.
      • Ayuso R.
      • Rosenstock J.
      • Raskin P.
      Relationship between lipoprotein levels and in vivo insulin action in normal young white men.
      • Laakso M.
      • Sarlund H.
      • Mykkanen L.
      Insulin resistance is associated with lipid and lipoprotein abnormalities in subjects with varying degrees of glucose tolerance.
      especially for low HDL-C and high triglycerides. Individual measures of LPIR, such as larger VLDL, smaller LDL, smaller HDL particle sizes, and higher concentrations of large VLDL and small LDL, and lower concentrations of large HDL, were individually associated with incident T2D in the WHS
      • Mora S.
      • Otvos J.D.
      • Rosenson R.S.
      • Pradhan A.
      • Buring J.E.
      • Ridker P.M.
      Lipoprotein particle size and concentration by nuclear magnetic resonance and incident type 2 diabetes in women.
      and other studies.
      • Mackey R.H.
      • Mora S.
      • Bertoni A.G.
      • et al.
      Lipoprotein particles and incident type 2 diabetes in the multi-ethnic study of atherosclerosis.
      • Festa A.
      • Williams K.
      • Hanley A.J.
      • et al.
      Nuclear magnetic resonance lipoprotein abnormalities in prediabetic subjects in the Insulin Resistance Atherosclerosis Study.
      • Garvey W.T.
      • Kwon S.
      • Zheng D.
      • et al.
      Effects of insulin resistance and type 2 diabetes on lipoprotein subclass particle size and concentration determined by nuclear magnetic resonance.
      • Goff Jr., D.C.
      • D'Agostino Jr., R.B.
      • Haffner S.M.
      • Otvos J.D.
      Insulin resistance and adiposity influence lipoprotein size and subclass concentrations. Results from the Insulin Resistance Atherosclerosis Study.
      In the normal liver, insulin inhibits VLDL secretion,
      • Lewis G.F.
      • Uffelman K.D.
      • Szeto L.W.
      • Steiner G.
      Effects of acute hyperinsulinemia on VLDL triglyceride and VLDL apoB production in normal weight and obese individuals.
      • Malmstrom R.
      • Packard C.J.
      • Caslake M.
      • et al.
      Effects of insulin and acipimox on VLDL1 and VLDL2 apolipoprotein B production in normal subjects.
      but not as efficiently when hepatic insulin resistance sets in. Then, large VLDL particles, rich in triglycerides, accumulate and suffer sequential lipolysis that leads to increasing concentrations of cholesterol-poor small LDL particles. HDL particles acquire triglycerides from VLDL in exchange for cholesterol under the action of cholesteryl ester transfer protein, which leads to lower concentrations of large HDL particles. Therefore, insulin-resistant dyslipoproteinemia results in higher concentrations of large VLDL and small LDL particles, and lower concentrations of large HDL particles, with corresponding effects on particle average size. Therefore, LPIR may better reflect the aggregate biology of lipoprotein insulin resistance rather than individual particles. Our data support the superiority of this approach, where the LPIR SD HR, 1.41 (95% CI: 1.31, 1.53), outranked HDL-C, triglycerides, and each individual LPIR sub-particle. Reassuringly, LPIR remained associated with T2D even after HDL-C and triglycerides adjustment and also in the subgroups of high HDL-C or low triglycerides. Thus, the LPIR signature reflects the complex biology of insulin resistance on lipoproteins metabolism.
      Glucose metabolism parameters are the benchmark for risk assessment and T2D diagnosis. Despite HbA1c adjustment, LPIR was strongly associated with T2D risk and represents an independent component of insulin resistance and T2D risk. As supporting data for that, within the optimal HbA1c (<5.7%) subgroup, for each SD LPIR increment, there was 51% higher risk for T2D (P for interaction .004). Pathophysiologically, very early insulin resistance enhances VLDL–triglyceride secretion in the liver when its glucose metabolism is normally responsive to insulin.
      • Sorensen L.P.
      • Sondergaard E.
      • Nellemann B.
      • Christiansen J.S.
      • Gormsen L.C.
      • Nielsen S.
      Increased VLDL-triglyceride secretion precedes impaired control of endogenous glucose production in obese, normoglycemic men.
      Hence, LPIR unveils an early underappreciated dimension of insulin resistance undetected by standard glucose metabolism parameters. By contrast, in the very high T2D risk group (ie, participants with HbA1c 5.7%–6.5%), the smaller magnitude (on a relative scale) of LPIR-related risk translated into a high absolute risk difference as these participants were at particularly high absolute risk for incident T2D (Fig. 5). Therefore, even smaller relative risk translates into clinically relevant incremental absolute risk.
      Regarding T2D risk assessment, LPIR enhanced the performance of a validated prediction score (FOS T2D).
      • Wilson P.W.
      • Meigs J.B.
      • Sullivan L.
      • et al.
      Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study.
      Importantly, the FOS score variables were refitted to WHS, which ensured high discriminative performance for FOS c-index, 0.878 for 10 years and 0.850 for 16 years. Hence, LPIR increment over FOS c-index, despite being small, is remarkable given the excellent FOS model performance. Also, the LPIR IDI for 10 years (0.006) is equal to the best performance IDI among 35 novel T2D risk factors, including a genetic risk score in the Atherosclerosis Risk in Communities Study.
      • Raynor L.A.
      • Pankow J.S.
      • Duncan B.B.
      • et al.
      Novel risk factors and the prediction of type 2 diabetes in the Atherosclerosis Risk in Communities (ARIC) study.
      Moreover, LPIR improved NRI at 16 years in the whole sample of our study, whereas none of the 35 novel risk factors in the ARIC study did.
      • Raynor L.A.
      • Pankow J.S.
      • Duncan B.B.
      • et al.
      Novel risk factors and the prediction of type 2 diabetes in the Atherosclerosis Risk in Communities (ARIC) study.
      Given that, LPIR seems promising for improving T2D risk prediction, especially in the intermediate risk group in which reclassification performance at 10 and 16 years was higher and clinically relevant. Despite the adequate calibration for LPIR only model at 16 years, by contrast to other models, FOS should remain as the standard practice for T2D risk prediction. The LPIR reclassification performance over FOS should be further tested in diverse populations.
      Our results may not extrapolate to men and non-Caucasian individuals, although they were consistent with MESA that included men and minorities. Despite no fasting plasma glucose in our analysis, HbA1c is a valid substitute. Undetected diabetes bias is highly unlikely as individuals with HbA1c ≥ 6.5% were excluded. Strengths include the large well-characterized population observed for a long follow-up period and reliable T2D ascertainment. Finally, the large number of T2D cases powered our study even for subgroup analyses.

      Conclusions

      In summary, LPIR is a novel metabolomic composite marker reflecting insulin-resistant dyslipoproteinemia, which was robustly associated with T2D risk in otherwise healthy middle-aged women during extended follow-up. Importantly, this finding extended even to those with very low risk of disease due to an optimal clinical profile.

      Acknowledgements

      Authors' contributions: P.H.N.H. researched literature, designed the study, made figures, analyzed and interpreted data, and wrote the article. O.V.D. designed the study and analyzed data. S.B.D. interpreted data and wrote the article. A.O.A. interpreted data and wrote the article. M.V.M. designed the study, analyzed data. P.M.R. interpreted data and wrote the article. N.R.C. designed the study and analyzed data. A.D.P. interpreted data and wrote the article. S.M. researched literature, designed the study, made figures, analyzed and interpreted data, and wrote the article.
      Sources of Funding: P.H.N.H. was funded by the Lemann Foundation. A.O.A. receives support from NIH T32 (HL007575). The Women's Health Study was funded by grants CA047988, HL043851, HL080467, HL099355, and UM1 CA182913. Additional funding was received from a charitable gift from the Molino Family Trust.
      S.M. received funding from the National Heart, Lung, and Blood Institute of the National Institute of Health (HL 117861).

      Disclosure

      P.H.N.H., O.V.D., S.B.D., A.O.A., and M.V.M. have nothing to disclose; P.M.R received research grant support from AstraZeneca, Novartis, Amgen, Pfizer, and NHLBI R01HL117861 and is listed as a co-inventor on patents held by the Brigham and Women's Hospital related to the use of inflammatory biomarkers in CVD (licensed to AstraZeneca and Siemens); N.R.C. has nothing to disclose; A.D.P. received research grant support from Kowa Research Institute and NIDDK R01 DK088078. S.M. received research grant support from Atherotech Diagnostics and NHLBI (HL 117861), is consultant to Lilly, Pfizer, Amgen, Quest Diagnostics, and Cerenis Therapeutics, and is co-inventor on a patent on the use of NMR-measured GlycA for predicting risk of colorectal cancer.

      Appendix.

      Figure thumbnail fx2
      Supplementary Figure 1Landmark plot for LPIR quartiles. Q1 (<30), Q2 (30–47), Q3 (48–67), and Q4 (>67). In the first time interval, individuals were censored after 10 years; the second interval included just those followed for more than 10 years.
      Supplementary Table 1Spearman correlation coefficients between LPIR and selected biomarkers in the study population
      AgeBMIHbA1cHDL-CTriglycerideshs-CRP
      0.070.440.18−0.660.750.37
      LDL-P sizeHDL-P sizeVLDL-P sizeLarge VLDL-P concentrationLarge HDL-P concentrationSmall LDL-P concentration
      −0.74−0.740.650.82−0.220.82
      BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein; LDL-P, low-density lipoprotein particles; LPIR, lipoprotein insulin resistance; HDL-P, high-density lipoprotein particles; VLDL-P, very low-density lipoprotein particles.
      Spearman correlation between LPIR and its individual components, all P values < .001.
      Supplementary Table 2LPIR and incident diabetes according to time intervals
      LPIR QuartilesQ1Q2Q3Q4P value for trendStandard unitsP value
      First 10 y of follow-up
       Events/N (%)47/6546 (0.72%)93/6386 (1.46%)275/657 (4.19%)678/6422 (10.56%)
       HR (95% CI)1.00 (ref.)1.19 (0.82, 1.71)1.68 (1.18, 2.37)2.16 (1.50, 3.12)<.00011.35 (1.21, 1.50)<.0001
      Last 10 years of follow-up
       Events/N (%)84/6189 (1.36%)154/5935 (2.59%)336/5919 (5.68%)592/5304 (11.16%)
       HR (95% CI)1.00 (ref.)1.33 (1.01, 1.76)1.89 (1.43, 2.51)2.35 (1.73, 3.21)<.00011.49 (1.34, 1.66)<.0001
      CI, confidence interval; HR, hazard ratio; LPIR, lipoprotein insulin resistance.
      Adjusted hazard ratios for LPIR quartiles and LPIR standard deviation units (23.4 score units). Proportional hazards assumption fulfilled for the first 10 years and beyond 10 years of follow-up. P values for trend according to quartiles median values.
      Supplementary Table 3Comparison of diabetes models on discrimination, calibration, and reclassification models
      ModelsDiscrimination C-indexC-index difference by LPIR additionGoodness-of-fit likelihood ratio χ2 (P)Calibration χ2 (P)IDI (95% CI)Relative IDI (95% CI)Categorical NRI (95% CI)Events % correct rec.Nonevents % correct. rec.
      For 10 y
       FOS0.878 (0.870, 0.886)ReferentReferent170.6 (<.001)ReferentReferentReferent
       FOS + LPIR0.880 (0.872, 0.888)0.002 (0.001, 0.004)39.97 (<.01)145.8 (<.001)0.006 (0.003, 0.008)0.037 (0.019, 0.056)0.012 (−0.007, 0.030)1.3%−0.1%
      For 16 y
       FOS0.850 (0.843, 0.857)ReferentReferent96.4 (<.001)ReferentReferentReferent
       FOS + LPIR0.853 (0.846, 0.859)0.002 (0.001, 0.004)87.49 (<.01)70.0 (<.001)0.009 (0.006, 0.010)0.043 (0.031, 0.056)0.028 (0.015, 0.043)2.5%0.3%
      CI, confidence interval; FOS, Framingham Offspring; IDI, integrated discrimination improvement; LPIR, lipoprotein insulin resistance; NRI, net reclassification improvement.
      Variables in the Framingham Offspring model, age, family history of T2D, body mass index, systolic blood pressure, antihypertensive medication, HDL, triglycerides, and HbA1c. C-index difference calculated by 10-fold cross-validation and the 95% CI was calculated by 1000 bootstrap replications. For calibration analysis Greenwood–Nam–D'Agostino test was applied, where a P value < .05 indicate a noncalibrated model. NRI risk limits for low, intermediate and high categories were 3.75% and 12.5% for 10 years, and 6% and 20% for 16 years.

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