Plasma Biomarker Profiles for Premature and Nonpremature Coronary Heart Disease in Women

BACKGROUND

Premature coronary heart disease (CHD) is a major cause of death in women. We aimed to characterize biomarker profiles of women who developed CHD before and after age 65 years.

METHODS

In the Women's Health Study (median follow-up 21.5 years), women were grouped by age and timing of incident CHD: baseline age <65 years with premature CHD by age 65 years (25 042 women; 447 events) and baseline age ≥65 years with nonpremature CHD (2982 women; 351 events). Associations of 44 baseline plasma biomarkers measured using standard assays and a nuclear magnetic resonance (NMR)-metabolomics assay were analyzed using Cox models adjusted for clinical risk factors.

RESULTS

Twelve biomarkers showed associations only with premature CHD and included lipoprotein(a), which was associated with premature CHD [adjusted hazard ratio (HR) per SD: 1.29 (95% CI 1.17-1.42)] but not with nonpremature CHD [1.09(0.98-1.22)](Pinteraction = 0.02). NMR-measured lipoprotein insulin resistance was associated with the highest risk of premature CHD [1.92 (1.52-2.42)] but was not associated with nonpremature CHD (Pinteraction <0.001). Eleven biomarkers showed stronger associations with premature vs nonpremature CHD, including apolipoprotein B. Nine NMR biomarkers showed no association with premature or nonpremature CHD, whereas 12 biomarkers showed similar significant associations with premature and nonpremature CHD, respectively, including low-density lipoprotein (LDL) cholesterol [1.30(1.20-1.45) and 1.22(1.10-1.35)] and C-reactive protein [1.34(1.19-1.50) and 1.25(1.08-1.44)].

CONCLUSIONS

In women, a profile of 12 biomarkers was selectively associated with premature CHD, driven by lipoprotein(a) and insulin-resistant atherogenic dyslipoproteinemia. This has implications for the development of biomarker panels to screen for premature CHD.

N-Glycosylation Profiles of Immunoglobulin G and Future Cardiovascular Events

BACKGROUND

Posttranslational glycosylation of IgG can modulate its inflammatory capacity through structural variations. We examined the association of baseline IgG N-glycans and an IgG glycan score with incident cardiovascular disease (CVD).

METHODS

IgG N-glycans were measured in 2 nested CVD case-control studies: JUPITER (Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin; NCT00239681; primary prevention; discovery; Npairs=162); and TNT trial (Treating to New Targets; NCT00327691; secondary prevention; validation; Npairs=397). Using conditional logistic regression, we investigated the association of future CVD with baseline IgG N-glycans and a glycan score adjusting for clinical risk factors (statin treatment, age, sex, race, lipids, hypertension, and smoking) in JUPITER. Significant associations were validated in TNT, using a similar model further adjusted for diabetes. Using least absolute shrinkage and selection operator regression, an IgG glycan score was derived in JUPITER as a linear combination of selected IgG N-glycans.

RESULTS

Six IgG N-glycans were associated with CVD in both studies: an agalactosylated glycan (IgG-GP4) was positively associated, while 3 digalactosylated glycans (IgG glycan peaks 12, 13, 14) and 2 monosialylated glycans (IgG glycan peaks 18, 20) were negatively associated with CVD after multiple testing correction (overall false discovery rate <0.05). Four selected IgG N-glycans comprised the IgG glycan score, which was associated with CVD in JUPITER (adjusted hazard ratio per glycan score SD, 2.08 [95% CI, 1.52-2.84]) and validated in TNT (adjusted hazard ratio per SD, 1.20 [95% CI, 1.03-1.39]). The area under the curve changed from 0.693 for the model without the score to 0.728 with the score in JUPITER (PLRT=1.1×10-6) and from 0.635 to 0.637 in TNT (PLRT=0.017).

CONCLUSIONS

An IgG N-glycan profile was associated with incident CVD in 2 populations (primary and secondary prevention), involving an agalactosylated glycan associated with increased risk of CVD, while several digalactosylated and sialylated IgG glycans associated with decreased risk. An IgG glycan score was positively associated with future CVD.

The Mediterranean Diet, Cardiometabolic Biomarkers, and Risk of All-Cause Mortality: A 25-Year Follow-Up Study of the Women's Health Study

Background

Higher consumption of Mediterranean diet (MED) intake has been associated with reduced risk of all-cause mortality but limited data are available examining long-term outcomes in women or the underlying molecular mechanisms of this inverse association in human populations. We aimed to investigate the association of MED intake with long-term risk of all-cause mortality in women and to better characterize the relative contribution of traditional and novel cardiometabolic factors to the MED-related risk reduction in morality.

Methods

In a prospective cohort study of 25,315 initially healthy women from the Women's Health Study, we assessed dietary MED intake using a validated semiquantitative food frequency questionnaire according to the usual 9-category measure of MED adherence. Baseline levels of more than thirty cardiometabolic biomarkers were measured using standard assays and targeted nuclear magnetic resonance spectroscopy, including lipids, lipoproteins, apolipoproteins, inflammation, glucose metabolism and insulin resistance, branched-chain amino acids, small metabolites, and clinical factors. Mortality and cause of death was ascertained prospectively through medical and death records.

Results

During a mean follow-up of 25 years, 3,879 deaths were ascertained. Compared to the reference group of low MED intake (0-3, approximately the bottom tertile), and adjusting for age, treatment, and energy intake, risk reductions were observed for the middle and upper MED groups with respective HRs of 0.84 (95% CI 0.78-0.90) and 0.77 (95% CI 0.70-0.84), p for trend <0.0001. Further adjusting for smoking, physical activity, alcohol intake and menopausal factors attenuated the risk reductions which remained significant with respective HRs of 0.92 (95% CI 0.85-0.99) and 0.89 (95% CI 0.82-0.98), p for trend 0.0011. Risk reductions were generally similar for CVD and non-CVD mortality. Small molecule metabolites (e.g., alanine and homocysteine) and inflammation made the largest contributions to lower mortality risk (accounting for 14.8% and 13.0% of the benefit of the MED-mortality association, respectively), followed by triglyceride-rich lipoproteins (10.2%), adiposity (10.2%) and insulin resistance (7.4%), with lesser contributions (<3%) from other pathways including branched-chain amino acids, high-density lipoproteins, low-density lipoproteins, glycemic measures, and hypertension.

Conclusions

In the large-scale prospective Women's Health Study of 25,315 initially healthy US women followed for 25 years, higher MED intake was associated with approximately one fifth relative risk reduction in mortality. The inverse association was only partially explained by known novel and traditional cardiometabolic factors.

Age of onset and cumulative risk of mental disorders: a cross-national analysis of population surveys from 29 countries

BACKGROUND

Information on the frequency and timing of mental disorder onsets across the lifespan is of fundamental importance for public health planning. Broad, cross-national estimates of this information from coordinated general population surveys were last updated in 2007. We aimed to provide updated and improved estimates of age-of-onset distributions, lifetime prevalence, and morbid risk.

METHODS

In this cross-national analysis, we analysed data from respondents aged 18 years or older to the World Mental Health surveys, a coordinated series of cross-sectional, face-to-face community epidemiological surveys administered between 2001 and 2022. In the surveys, the WHO Composite International Diagnostic Interview, a fully structured psychiatric diagnostic interview, was used to assess age of onset, lifetime prevalence, and morbid risk of 13 DSM-IV mental disorders until age 75 years across surveys by sex. We did not assess ethnicity. The surveys were geographically clustered and weighted to adjust for selection probability, and standard errors of incidence rates and cumulative incidence curves were calculated using the jackknife repeated replications simulation method, taking weighting and geographical clustering of data into account.

FINDINGS

We included 156 331 respondents from 32 surveys in 29 countries, including 12 low-income and middle-income countries and 17 high-income countries, and including 85 308 (54·5%) female respondents and 71 023 (45·4%) male respondents. The lifetime prevalence of any mental disorder was 28·6% (95% CI 27·9-29·2) for male respondents and 29·8% (29·2-30·3) for female respondents. Morbid risk of any mental disorder by age 75 years was 46·4% (44·9-47·8) for male respondents and 53·1% (51·9-54·3) for female respondents. Conditional probabilities of first onset peaked at approximately age 15 years, with a median age of onset of 19 years (IQR 14-32) for male respondents and 20 years (12-36) for female respondents. The two most prevalent disorders were alcohol use disorder and major depressive disorder for male respondents and major depressive disorder and specific phobia for female respondents.

INTERPRETATION

By age 75 years, approximately half the population can expect to develop one or more of the 13 mental disorders considered in this Article. These disorders typically first emerge in childhood, adolescence, or young adulthood. Services should have the capacity to detect and treat common mental disorders promptly and to optimise care that suits people at these crucial parts of the life course.

FUNDING

None.

Evaluation of a Model to Target High-risk Psychiatric Inpatients for an Intensive Postdischarge Suicide Prevention Intervention

Importance

The months after psychiatric hospital discharge are a time of high risk for suicide. Intensive postdischarge case management, although potentially effective in suicide prevention, is likely to be cost-effective only if targeted at high-risk patients. A previously developed machine learning (ML) model showed that postdischarge suicides can be predicted from electronic health records and geospatial data, but it is unknown if prediction could be improved by adding additional information.

Objective

To determine whether model prediction could be improved by adding information extracted from clinical notes and public records.

Design, Setting, and Participants

Models were trained to predict suicides in the 12 months after Veterans Health Administration (VHA) short-term (less than 365 days) psychiatric hospitalizations between the beginning of 2010 and September 1, 2012 (299 050 hospitalizations, with 916 hospitalizations followed within 12 months by suicides) and tested in the hospitalizations from September 2, 2012, to December 31, 2013 (149 738 hospitalizations, with 393 hospitalizations followed within 12 months by suicides). Validation focused on net benefit across a range of plausible decision thresholds. Predictor importance was assessed with Shapley additive explanations (SHAP) values. Data were analyzed from January to August 2022.

Main Outcomes and Measures

Suicides were defined by the National Death Index. Base model predictors included VHA electronic health records and patient residential data. The expanded predictors came from natural language processing (NLP) of clinical notes and a social determinants of health (SDOH) public records database.

Results

The model included 448 788 unique hospitalizations. Net benefit over risk horizons between 3 and 12 months was generally highest for the model that included both NLP and SDOH predictors (area under the receiver operating characteristic curve range, 0.747-0.780; area under the precision recall curve relative to the suicide rate range, 3.87-5.75). NLP and SDOH predictors also had the highest predictor class-level SHAP values (proportional SHAP = 64.0% and 49.3%, respectively), although the single highest positive variable-level SHAP value was for a count of medications classified by the US Food and Drug Administration as increasing suicide risk prescribed the year before hospitalization (proportional SHAP = 15.0%).

Conclusions and Relevance

In this study, clinical notes and public records were found to improve ML model prediction of suicide after psychiatric hospitalization. The model had positive net benefit over 3-month to 12-month risk horizons for plausible decision thresholds. Although caution is needed in inferring causality based on predictor importance, several key predictors have potential intervention implications that should be investigated in future studies.

Association of Physical Activity With Bioactive Lipids and Cardiovascular Events

BACKGROUND

To clarify the mechanisms underlying physical activity (PA)-related cardioprotection, we examined the association of PA with plasma bioactive lipids (BALs) and cardiovascular disease (CVD) events. We additionally performed genome-wide associations.

METHODS

PA-bioactive lipid associations were examined in VITAL (VITamin D and OmegA-3 TriaL)-clinical translational science center (REGISTRATION: URL: https://www.

CLINICALTRIALS

gov; Unique identifier: NCT01169259; N=1032) and validated in JUPITER (Justification for the Use of statins in Prevention: an Intervention Trial Evaluating Rosuvastatin)-NC (NCT00239681; N=589), using linear models adjusted for age, sex, race, low-density lipoprotein-cholesterol, total-C, and smoking. Significant BALs were carried over to examine associations with incident CVD in 2 nested CVD case-control studies: VITAL-CVD (741 case-control pairs) and JUPITER-CVD (415 case-control pairs; validation).

RESULTS

We detected 145 PA-bioactive lipid validated associations (false discovery rate <0.1). Annotations were found for 6 of these BALs: 12,13-diHOME, 9,10-diHOME, lysoPC(15:0), oxymorphone-3b-D-glucuronide, cortisone, and oleoyl-glycerol. Genetic analysis within JUPITER-NC showed associations of 32 PA-related BALs with 22 single-nucleotide polymorphisms. From PA-related BALs, 12 are associated with CVD.

CONCLUSIONS

We identified a PA-related bioactive lipidome profile out of which 12 BALs also had opposite associations with incident CVD events.

Association of Lipid, Inflammatory, and Metabolic Biomarkers With Age at Onset for Incident Coronary Heart Disease in Women

Importance

Risk profiles for premature coronary heart disease (CHD) are unclear.

Objective

To examine baseline risk profiles for incident CHD in women by age at onset.

Design, Setting, and Participants

A prospective cohort of US female health professionals participating in the Women's Health Study was conducted; median follow-up was 21.4 years. Participants included 28 024 women aged 45 years or older without known cardiovascular disease. Baseline profiles were obtained from April 30, 1993, to January 24, 1996, and analyses were conducted from October 1, 2017, to October 1, 2020.

Exposures

More than 50 clinical, lipid, inflammatory, and metabolic risk factors and biomarkers.

Main Outcomes and Measures

Four age groups were examined (<55, 55 to <65, 65 to <75, and ≥75 years) for CHD onset, and adjusted hazard ratios (aHRs) were calculated using stratified Cox proportional hazard regression models with age as the time scale and adjusting for clinical factors. Women contributed to different age groups over time.

Results

Of the clinical factors in the women, diabetes had the highest aHR for CHD onset at any age, ranging from 10.71 (95% CI, 5.57-20.60) at CHD onset in those younger than 55 years to 3.47 (95% CI, 2.47-4.87) at CHD onset in those 75 years or older. Risks that were also noted for CHD onset in participants younger than 55 years included metabolic syndrome (aHR, 6.09; 95% CI, 3.60-10.29), hypertension (aHR, 4.58; 95% CI, 2.76-7.60), obesity (aHR, 4.33; 95% CI, 2.31-8.11), and smoking (aHR, 3.92; 95% CI, 2.32-6.63). Myocardial infarction in a parent before age 60 years was associated with 1.5- to 2-fold risk of CHD in participants up to age 75 years. From approximately 50 biomarkers, lipoprotein insulin resistance had the highest standardized aHR: 6.40 (95% CI, 3.14-13.06) for CHD onset in women younger than 55 years, attenuating with age. In comparison, weaker but significant associations with CHD in women younger than 55 years were noted (per SD increment) for low-density lipoprotein cholesterol (aHR, 1.38; 95% CI, 1.10-1.74), non-high-density lipoprotein cholesterol (aHR, 1.67; 95% CI, 1.36-2.04), apolipoprotein B (aHR, 1.89; 95% CI, 1.52-2.35), triglycerides (aHR, 2.14; 95% CI, 1.72-2.67), and inflammatory biomarkers (1.2- to 1.8-fold)-all attenuating with age. Some biomarkers had similar CHD age associations (eg, physical inactivity, lipoprotein[a], total high-density lipoprotein particles), while a few had no association with CHD onset at any age. Most risk factors and biomarkers had associations that attenuated with increasing age at onset.

Conclusions and Relevance

In this cohort study, diabetes and insulin resistance, in addition to hypertension, obesity, and smoking, appeared to be the strongest risk factors for premature onset of CHD. Most risk factors had attenuated relative rates at older ages.

Association of the Mediterranean Diet With Onset of Diabetes in the Women's Health Study

IMPORTANCE

Higher Mediterranean diet (MED) intake has been associated with reduced risk of type 2 diabetes, but underlying biological mechanisms are unclear.

OBJECTIVE

To characterize the relative contribution of conventional and novel biomarkers in MED-associated type 2 diabetes risk reduction in a US population.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study was conducted among 25 317 apparently healthy women. The participants with missing information regarding all traditional and novel metabolic biomarkers or those with baseline diabetes were excluded. Participants were invited for baseline assessment between September 1992 and May 1995. Data were collected from November 1992 to December 2017 and analyzed from December 2018 to December 2019.

EXPOSURES

MED intake score (range, 0 to 9) was computed from self-reported dietary intake, representing adherence to Mediterranean diet intake.

MAIN OUTCOMES AND MEASURES

Incident cases of type 2 diabetes, identified through annual questionnaires; reported cases were confirmed by either telephone interview or supplemental questionnaire. Proportion of reduced risk of type 2 diabetes explained by clinical risk factors and a panel of 40 biomarkers that represent different physiological pathways was estimated.

RESULTS

The mean (SD) age of the 25 317 female participants was 52.9 (9.9) years, and they were followed up for a mean (SD) of 19.8 (5.8) years. Higher baseline MED intake (score ≥6 vs ≤3) was associated with as much as a 30% lower type 2 diabetes risk (age-adjusted and energy-adjusted hazard ratio, 0.70; 95% CI, 0.62-0.79; when regression models were additionally adjusted with body mass index [BMI]: hazard ratio, 0.85; 95% CI, 0.76-0.96). Biomarkers of insulin resistance made the largest contribution to lower risk (accounting for 65.5% of the MED-type 2 diabetes association), followed by BMI (55.5%), high-density lipoprotein measures (53.0%), and inflammation (52.5%), with lesser contributions from branched-chain amino acids (34.5%), very low-density lipoprotein measures (32.0%), low-density lipoprotein measures (31.0%), blood pressure (29.0%), and apolipoproteins (23.5%), and minimal contribution (≤2%) from hemoglobin A1c. In post hoc subgroup analyses, the inverse association of MED diet with type 2 diabetes was seen only among women who had BMI of at least 25 at baseline but not those who had BMI of less than 25 (eg, women with BMI <25, age- and energy-adjusted HR for MED score ≥6 vs ≤3, 1.01; 95% CI, 0.77-1.33; P for trend = .92; women with BMI ≥25: HR, 0.76; 95% CI, 0.67-0.87; P for trend < .001).

CONCLUSIONS AND RELEVANCE

In this cohort study, higher MED intake scores were associated with a 30% relative risk reduction in type 2 diabetes during a 20-year period, which could be explained in large part by biomarkers of insulin resistance, BMI, lipoprotein metabolism, and inflammation.

One-Year Effects of Omega-3 Treatment on Fatty Acids, Oxylipins, and Related Bioactive Lipids and Their Associations with Clinical Lipid and Inflammatory Biomarkers: Findings from a Substudy of the Vitamin D and Omega-3 Trial (VITAL)

Omega-3 (n-3) treatment may lower cardiovascular risk, yet its effects on the circulating lipidome and relation to cardiovascular risk biomarkers are unclear. We hypothesized that n-3 treatment is associated with favorable changes in downstream fatty acids (FAs), oxylipins, bioactive lipids, clinical lipid and inflammatory biomarkers. We examined these VITAL200, a nested substudy of 200 subjects balanced on demographics and treatment and randomly selected from the Vitamin D and Omega-3 Trial (VITAL). VITAL is a randomized double-blind trial of 840 mg/d eicosapentaenoic acid (EPA) + docosahexaenoic acid (DHA) vs. placebo among 25,871 individuals. Small polar bioactive lipid features, oxylipins and FAs from plasma and red blood cells were measured using three independent assaying techniques at baseline and one year. The Women's Health Study (WHS) was used for replication with dietary n-3 intake. Randomized n-3 treatment led to changes in 143 FAs, oxylipins and bioactive lipids (False Discovery Rate (FDR) < 0.05 in VITAL200, validated (p-values < 0.05)) in WHS with increases in 95 including EPA, DHA, n-3 docosapentaenoic acid (DPA-n3), and decreases in 48 including DPA-n6, dihomo gamma linolenic (DGLA), adrenic and arachidonic acids. N-3 related changes in the bioactive lipidome were heterogeneously associated with changes in clinical lipid and inflammatory biomarkers. N-3 treatment significantly modulates the bioactive lipidome, which may contribute to its clinical benefits.

Anti-Inflammatory HDL Function, Incident Cardiovascular Events, and Mortality: A Secondary Analysis of the JUPITER Randomized Clinical Trial

Background

High‐density lipoprotein (HDL) cholesterol has inverse association with cardiovascular disease. HDL possesses anti‐inflammatory properties in vitro, but it is unknown whether this may be protective in individuals with inflammation.

Methods and Results

The functional capacity of HDL to inhibit oxidation of oxidized low‐density lipoprotein (ie, the HDL inflammatory index; HII) was measured at baseline and 12 months after random allocation to rosuvastatin or placebo in a nested case‐control study of the JUPITER (Justification for the Use of Statins in Prevention: An Intervention Evaluating Rosuvastatin) trial. There were 517 incident cases of cardiovascular disease and all‐cause mortality compared to 517 age‐ and sex‐matched controls. Multivariable conditional logistic regression was used to examine associations of HII with events. Median baseline HII was 0.54 (interquartile range, 0.50–0.59). Twelve months of rosuvastatin decreased HII by a mean of 5.3% (95% CI, −8.9% to −1.7%; P=0.005) versus 1.3% (95% CI, −6.5% to 4.0%; P=0.63) with placebo (P=0.22 for between‐group difference). HII had a nonlinear relationship with incident events. Compared with the reference group (HII 0.5–1.0) with the lowest event rates, participants with baseline HII ≤0.5 had significantly increased risk of cardiovascular disease/mortality (adjusted hazard ratio, 1.53; 95% CI, 1.06–2.21; P=0.02). Furthermore, there was significant (P=0.002) interaction for HDL particle number with HII, such that having more HDL particles was associated with decreased risk only when HDL was anti‐inflammatory.

Conclusions

In JUPITER participants recruited on the basis of chronic inflammation, HII was associated with incident cardiovascular disease/mortality, with an optimal anti‐inflammatory HII range between 0.5 and 1.0. This nonlinear relationship of anti‐inflammatory HDL function with risk may account in part for the HDL paradox.

Registration

URL: https://www.clini​caltr​ials.gov; Unique identifier: NCT00239681.

Probabilistic identification of saccharide moieties in biomolecules and their protein complexes

The chemical composition of saccharide complexes underlies their biomedical activities as biomarkers for cardiometabolic disease, various types of cancer, and other conditions. However, because these molecules may undergo major structural modifications, distinguishing between compounds of saccharide and non-saccharide origin becomes a challenging computational problem that hinders the aggregation of information about their bioactive moieties. We have developed an algorithm and software package called "Cheminformatics Tool for Probabilistic Identification of Carbohydrates" (CTPIC) that analyzes the covalent structure of a compound to yield a probabilistic measure for distinguishing saccharides and saccharide-derivatives from non-saccharides. CTPIC analysis of the RCSB Ligand Expo (database of small molecules found to bind proteins in the Protein Data Bank) led to a substantial increase in the number of ligands characterized as saccharides. CTPIC analysis of Protein Data Bank identified 7.7% of the proteins as saccharide-binding. CTPIC is freely available as a webservice at (http://ctpic.nmrfam.wisc.edu).

Pleiotropy-Based Decomposition of Genetic Risk Scores: Association and Interaction Analysis for Type 2 Diabetes and CAD

Genetic risk for a disease in the population may be represented as a genetic risk score (GRS) constructed as the sum of inherited risk alleles, weighted by allelic effects established in an independent population. While this formulation captures overall genetic risk, it typically does not address risk due to specific biological mechanisms or pathways that may nevertheless be important for interpretation or treatment response. Here, a GRS for disease is resolved into independent or nearly independent components pertaining to biological mechanisms inferred from pleiotropic relationships. The component GRSs' weights are derived from the singular value decomposition (SVD) of the matrix of appropriately scaled genetic effects, i.e., beta coefficients, of the disease variants across a panel of the disease-related phenotypes. The SVD-based formalism also associates combinations of disease-related phenotypes with inferred disease pathways. Applied to incident type 2 diabetes (T2D) in the Women's Genome Health Study (N = 23,294), component GRSs discriminate glycemic control and lipid-based genetic risk, while revealing significant interactions between specific components and BMI or physical activity, the latter not observed with a GRS for overall T2D genetic liability. Applied to coronary artery disease (CAD) in both the WGHS and in JUPITER (N = 8,749), a randomized trial of rosuvastatin for primary prevention of CVD, component GRSs discriminate genetic risk associated with LDL-C from risk associated with reciprocal genetic effects on triglycerides and HDL-C. They also inform the pharmacogenetics of statin treatment by demonstrating that benefit from rosuvastatin is as strongly related to genetic risk from triglycerides and HDL-C as from LDL-C.

Comparison of nonfasting and fasting lipoprotein subfractions and size in 15,397 apparently healthy individuals: An analysis from the VITamin D and OmegA-3 TriaL

BACKGROUND

Elevated postprandial triglycerides reflect a proatherogenic milieu, but underlying mechanisms are unclear.

OBJECTIVE

We examined differences between fasting and nonfasting profiles of directly measured lipoprotein size and subfractions to assess if postprandial triglycerides reflected increases in very low density lipoprotein (VLDL), intermediate density lipoprotein (IDL) and remnants, or small dense lipid depleted LDL (sdLDL) particles.

METHODS

We conducted a cross-sectional analysis of 15,397 participants (10,135 fasting; 5262 nonfasting [<8 hours since last meal]) from the VITamin D and OmegA-3 TriaL. Baseline cholesterol subfractions were measured by the vertical auto profile method and particle subfractions by ion mobility. We performed multivariable linear regression adjusting for cardiovascular and lipoprotein-modifying risk factors.

RESULTS

Mean age (SD) was 68.0 years (±7.0), with 50.9% women. Adjusted mean triglyceride concentrations were higher nonfasting by 17.8 ± 1.3%, with higher nonfasting levels of directly measured VLDL cholesterol (by 3.5 ± 0.6%) and total VLDL particles (by 2.0 ± 0.7%), specifically large VLDL (by 12.3 ± 1.3%) and medium VLDL particles (by 5.3 ± 0.8%), all P < .001. By contrast, lower concentrations of low density lipoprotein (LDL) and IDL cholesterol and particles were noted for nonfasting participants. sdLDL cholesterol levels and particle concentrations showed no statistically significant difference by fasting status (-1.3 ± 2.1% and 0.07 ± 0.6%, respectively, P > .05).

CONCLUSIONS

Directly measured particle and cholesterol concentrations of VLDL, not sdLDL, were higher nonfasting and may partly contribute to the proatherogenicity of postprandial hypertriglyceridemia. These differences, although statistically significant, were small and may not fully explain the increased risk of postprandial hypertriglyceridemia.

Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System

There is a very high suicide rate in the year after psychiatric hospital discharge. Intensive postdischarge case management programs can address this problem but are not cost-effective for all patients. This issue can be addressed by developing a risk model to predict which inpatients might need such a program. We developed such a model for the 391,018 short-term psychiatric hospital admissions of US veterans in Veterans Health Administration (VHA) hospitals 2010-2013. Records were linked with the National Death Index to determine suicide within 12 months of hospital discharge (n=771). The Super Learner ensemble machine learning method was used to predict these suicides for time horizon between 1 week and 12 months after discharge in a 70% training sample. Accuracy was validated in the remaining 30% holdout sample. Predictors included VHA administrative variables and small area geocode data linked to patient home addresses. The models had AUC=.79-.82 for time horizons between 1 week and 6 months and AUC=.74 for 12 months. An analysis of operating characteristics showed that 22.4%-32.2% of patients who died by suicide would have been reached if intensive case management was provided to the 5% of patients with highest predicted suicide risk. Positive predictive value (PPV) at this higher threshold ranged from 1.2% over 12 months to 3.8% per case manager year over 1 week. Focusing on the low end of the risk spectrum, the 40% of patients classified as having lowest risk account for 0%-9.7% of suicides across time horizons. Variable importance analysis shows that 51.1% of model performance is due to psychopathological risk factors accounted, 26.2% to social determinants of health, 14.8% to prior history of suicidal behaviors, and 6.6% to physical disorders. The paper closes with a discussion of next steps in refining the model and prospects for developing a parallel precision treatment model.

Serum 25-hydroxyvitamin D in the VITamin D and OmegA-3 TriaL (VITAL): Clinical and demographic characteristics associated with baseline and change with randomized vitamin D treatment

BACKGROUND

The VITamin D and OmegA-3 TriaL (VITAL) is a completed randomized, placebo-controlled trial of vitamin D3 (2000 IU/day) and marine omega-3 (1 g/day) supplements in the primary prevention of cancer and cardiovascular disease. Here we examine baseline and change in 25-hydroxyvitamin D (25(OH)D) and related biomarkers with randomized treatment and by clinical factors.

METHODS

Baseline 25(OH)D was measured in 15,804 participants (mean age 68 years.; 50.8% women; 15.7% African Americans) and in 1660 1-year follow-up samples using liquid chromatography-tandem mass spectrometry and chemiluminescence. Calcium and parathyroid hormone (iPTH) were measured by chemiluminescence and spectrophotometry respectively.

RESULTS

Mean baseline total 25(OH)D (ng/mL ± SD) was 30.8 ± 10.0 ng/mL, and correlated inversely with iPTH (r = -0.28), p < .001. After adjusting for clinical factors, 25(OH)D (ng/mL ± SE) was lower in men vs women (29.7 ± 0.30 vs 31.4 ± 0.30, p < .0001) and in African Americans vs whites (27.9 ± 0.29 vs 32.5 ± 0.22, p < .0001). It was also lower with increasing BMI, smoking, and latitude, and varied by season. Mean 1-year 25(OH)D increased by 11.9 ng/mL in the active group and decreased by 0.7 ng/mL in placebo. The largest increases were noted among individuals with low baseline and African Americans. Results were similar for chemiluminescent immunoassay. Mean calcium was unchanged, and iPTH decreased with treatment.

CONCLUSION

In VITAL, baseline 25(OH)D varied by clinical subgroups, was lower in men and African Americans. Concentrations increased with vitamin D supplementation, with the greatest increases in those with lower baseline 25(OH)D. The seasonal trends in 25(OH)D, iPTH, and calcium may be relevant when interpreting 25(OH)D levels for clinical treatment decisions.

CLINICAL TRIAL REGISTRATION

VITAL ClinicalTrials.gov number NCT01169259.

Statistical Workflow for Feature Selection in Human Metabolomics Data

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations.

Measures for evaluation of prognostic improvement under multivariate normality for nested and nonnested models

When comparing performances of two risk prediction models, several metrics exist to quantify prognostic improvement, including the change in the area under the Receiver Operating Characteristic curve, the Integrated Discrimination Improvement, the Net Reclassification Index at event rate, the change in Standardized Net Benefit, the change in Brier score, and the change in scaled Brier score. We explore the behavior and interrelationships between these metrics under multivariate normality in nested and nonnested model comparisons. We demonstrate that, within the framework of linear discriminant analysis, all six statistics are functions of squared Mahalanobis distance, a robust metric that properly measures discrimination by quantifying the separation between the risk scores of events and nonevents. These relationships are important for overall interpretability and clinical usefulness. Through simulation, we demonstrate that the performance of the theoretical estimators under normality is comparable or superior to empirical estimation methods typically used by investigators. In particular, the theoretical estimators for the Net Reclassification Index and the change in Standardized Net Benefit exhibit less variability in their estimates as compared to their empirically estimated counterparts. Finally, we explore how these metrics behave with potentially nonnormal data by applying these methods in a practical example based on the sex-specific cardiovascular disease risk models from the Framingham Heart Study. Our findings aim to give greater insight into the behavior of these measures and the connections existing among them and to provide additional estimation methods with less variability for the Net Reclassification Index and the change in Standardized Net Benefit.

Circulating Branched-Chain Amino Acids and Incident Cardiovascular Disease in a Prospective Cohort of US Women

BACKGROUND

Circulating branched-chain amino acids (BCAAs; isoleucine, leucine, and valine) are strong predictors of type 2 diabetes mellitus (T2D), but their association with cardiovascular disease (CVD) is uncertain. We hypothesized that plasma BCAAs are positively associated with CVD risk and evaluated whether this was dependent on an intermediate diagnosis of T2D.

METHODS

Participants in the Women's Health Study prospective cohort were eligible if free of CVD at baseline blood collection (n=27 041). Plasma metabolites were measured via nuclear magnetic resonance spectroscopy. Multivariable Cox regression models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for BCAAs with incident CVD (myocardial infarction, stroke, and coronary revascularization).

RESULTS

We confirmed 2207 CVD events over a mean 18.6 years of follow-up. Adjusting for age, body mass index, and other established CVD risk factors, total BCAAs were positively associated with CVD (per SD: HR, 1.13; 95% CI, 1.08-1.18), comparable to LDL-C (low-density lipoprotein cholesterol) with CVD (per SD: HR, 1.12; 95% CI, 1.07-1.17). BCAAs were associated with coronary events (myocardial infarction: HR, 1.16; 95% CI, 1.06-1.26; revascularization: HR, 1.17; 95% CI, 1.11-1.25), and borderline significant association with stroke (HR, 1.07; 95% CI, 0.99-1.15). The BCAA-CVD association was greater (P interaction=0.036) among women who developed T2D before CVD (HR, 1.20; 95% CI, 1.08-1.32) versus women without T2D (HR, 1.08; 95% CI, 1.03-1.14). Adjusting for LDL-C, an established CVD risk factor, did not attenuate these findings; however, adjusting for HbA1c and insulin resistance eliminated the associations of BCAAs with CVD.

CONCLUSIONS

Circulating plasma BCAAs were positively associated with incident CVD in women. Impaired BCAA metabolism may capture the long-term risk of the common cause underlying T2D and CVD.

Assessment of Risk Factors and Biomarkers Associated With Risk of Cardiovascular Disease Among Women Consuming a Mediterranean Diet

IMPORTANCE

Higher Mediterranean diet (MED) intake has been associated with lower risk of cardiovascular disease (CVD), but limited data are available about the underlying molecular mechanisms of this inverse disease association in human populations.

OBJECTIVE

To better characterize the relative contribution of traditional and novel factors to the MED-related risk reduction in CVD events in a US population.

DESIGN, SETTING, AND PARTICIPANTS

Using a prospective cohort design, baseline MED intake was assessed in 25 994 initially healthy US women in the Women's Health Study who were followed up to 12 years. Potential mediating effects of a panel of 40 biomarkers were evaluated, including lipids, lipoproteins, apolipoproteins, inflammation, glucose metabolism and insulin resistance, branched-chain amino acids, small-molecule metabolites, and clinical factors. Baseline study information and samples were collected between April 30, 1993, and January 24, 1996. Analyses were conducted between August 1, 2017, and October 30, 2018.

EXPOSURES

Intake of MED is a 9-category measure of adherence to a Mediterranean dietary pattern. Participants were categorized into 3 levels based on their adherence to the MED.

MAIN OUTCOMES AND MEASURES

Incident CVD confirmed through medical records and the proportion of CVD risk reduction explained by mediators.

RESULTS

Among 25 994 women (mean [SD] age, 54.7 [7.1] years), those with low, middle, and upper MED intakes composed 39.0%, 36.2%, and 24.8% of the study population and experienced 428 (4.2%), 356 (3.8%), and 246 (3.8%) incident CVD events, respectively. Compared with the reference group who had low MED intake, CVD risk reductions were observed for the middle and upper groups, with respective HRs of 0.77 (95% CI, 0.67-0.90) and 0.72 (95% CI, 0.61-0.86) (P for trend < .001). The largest mediators of the CVD risk reduction of MED intake were biomarkers of inflammation (accounting for 29.2% of the MED-CVD association), glucose metabolism and insulin resistance (27.9%), and body mass index (27.3%), followed by blood pressure (26.6%), traditional lipids (26.0%), high-density lipoprotein measures (24.0%) or very low-density lipoprotein measures (20.8%), with lesser contributions from low-density lipoproteins (13.0%), branched-chain amino acids (13.6%), apolipoproteins (6.5%), or other small-molecule metabolites (5.8%).

CONCLUSIONS AND RELEVANCE

In this study, higher MED intake was associated with approximately one-fourth relative risk reduction in CVD events, which could be explained in part by known risk factors, both traditional and novel.

Clinical risk reclassification at 10 years

Three papers in this issue focus on the role of calibration in model fit statistics, including the net reclassification improvement (NRI) and integrated discrimination improvement (IDI). This commentary reviews the development of such reclassification statistics along with more recent advances in our understanding of these measures. We show how the two-category NRI and the IDI are affected by changes in the event rate in theory and in an applied example. We also describe the role of calibration and how it may be assessed. Finally, we discuss the relevance of the event rate NRI for clinical use. Copyright © 2017 John Wiley & Sons, Ltd.

Reclassification calibration test for censored survival data: performance and comparison to goodness-of-fit criteria

BACKGROUND

The risk reclassification table assesses clinical performance of a biomarker in terms of movements across relevant risk categories. The Reclassification-Calibration (RC) statistic has been developed for binary outcomes, but its performance for survival data with moderate to high censoring rates has not been evaluated.

METHODS

We develop an RC statistic for survival data with higher censoring rates using the Greenwood-Nam-D'Agostino approach (RC-GND). We examine its performance characteristics and compare its performance and utility to the Hosmer-Lemeshow goodness-of-fit test under various assumptions about the censoring rate and the shape of the baseline hazard.

RESULTS

The RC-GND test was robust to high (up to 50%) censoring rates and did not exceed the targeted 5% Type I error in a variety of simulated scenarios. It achieved 80% power to detect better calibration with respect to clinical categories when an important predictor with a hazard ratio of at least 1.7 to 2.2 was added to the model, while the Hosmer-Lemeshow goodness of fit (gof) test had power of 5% in this scenario.

CONCLUSIONS

The RC-GND test should be used to test the improvement in calibration with respect to clinically-relevant risk strata. When an important predictor is omitted, the Hosmer-Lemeshow goodness-of-fit test is usually not significant, while the RC-GND test is sensitive to such an omission.

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

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.

Asymptotic distribution of ∆AUC, NRIs, and IDI based on theory of U-statistics

The change in area under the curve (∆AUC), the integrated discrimination improvement (IDI), and net reclassification index (NRI) are commonly used measures of risk prediction model performance. Some authors have reported good validity of associated methods of estimating their standard errors (SE) and construction of confidence intervals, whereas others have questioned their performance. To address these issues, we unite the ∆AUC, IDI, and three versions of the NRI under the umbrella of the U-statistics family. We rigorously show that the asymptotic behavior of ∆AUC, NRIs, and IDI fits the asymptotic distribution theory developed for U-statistics. We prove that the ∆AUC, NRIs, and IDI are asymptotically normal, unless they compare nested models under the null hypothesis. In the latter case, asymptotic normality and existing SE estimates cannot be applied to ∆AUC, NRIs, or IDI. In the former case, SE formulas proposed in the literature are equivalent to SE formulas obtained from U-statistics theory if we ignore adjustment for estimated parameters. We use Sukhatme-Randles-deWet condition to determine when adjustment for estimated parameters is necessary. We show that adjustment is not necessary for SEs of the ∆AUC and two versions of the NRI when added predictor variables are significant and normally distributed. The SEs of the IDI and three-category NRI should always be adjusted for estimated parameters. These results allow us to define when existing formulas for SE estimates can be used and when resampling methods such as the bootstrap should be used instead when comparing nested models. We also use the U-statistic theory to develop a new SE estimate of ∆AUC. Copyright © 2017 John Wiley & Sons, Ltd.

Cholesterol Efflux Capacity, High-Density Lipoprotein Particle Number, and Incident Cardiovascular Events: An Analysis From the JUPITER Trial (Justification for the Use of Statins in Prevention: An Intervention Trial Evaluating Rosuvastatin)

BACKGROUND

Recent failures of drugs that raised high-density lipoprotein (HDL) cholesterol levels to reduce cardiovascular events in clinical trials have led to increased interest in alternative indices of HDL quality, such as cholesterol efflux capacity, and HDL quantity, such as HDL particle number. However, no studies have directly compared these metrics in a contemporary population that includes potent statin therapy and low low-density lipoprotein cholesterol.

METHODS

HDL cholesterol levels, apolipoprotein A-I, cholesterol efflux capacity, and HDL particle number were assessed at baseline and 12 months in a nested case-control study of the JUPITER trial (Justification for the Use of Statins in Prevention: An Intervention Trial Evaluating Rosuvastatin), a randomized primary prevention trial that compared rosuvastatin treatment to placebo in individuals with normal low-density lipoprotein cholesterol but increased C-reactive protein levels. In total, 314 cases of incident cardiovascular disease (CVD) (myocardial infarction, unstable angina, arterial revascularization, stroke, or cardiovascular death) were compared to age- and gender-matched controls. Conditional logistic regression models adjusting for risk factors evaluated associations between HDL-related biomarkers and incident CVD.

RESULTS

Cholesterol efflux capacity was moderately correlated with HDL cholesterol, apolipoprotein A-I, and HDL particle number (Spearman r= 0.39, 0.48, and 0.39 respectively; P<0.001). Baseline HDL particle number was inversely associated with incident CVD (adjusted odds ratio per SD increment [OR/SD], 0.69; 95% confidence interval [CI], 0.56-0.86; P<0.001), whereas no significant association was found for baseline cholesterol efflux capacity (OR/SD, 0.89; 95% CI, 0.72-1.10; P=0.28), HDL cholesterol (OR/SD, 0.82; 95% CI, 0.66-1.02; P=0.08), or apolipoprotein A-I (OR/SD, 0.83; 95% CI, 0.67-1.03; P=0.08). Twelve months of rosuvastatin (20 mg/day) did not change cholesterol efflux capacity (average percentage change -1.5%, 95% CI, -13.3 to +10.2; P=0.80), but increased HDL cholesterol (+7.7%), apolipoprotein A-I (+4.3%), and HDL particle number (+5.2%). On-statin cholesterol efflux capacity was inversely associated with incident CVD (OR/SD, 0.62; 95% CI, 0.42-0.92; P=0.02), although HDL particle number again emerged as the strongest predictor (OR/SD, 0.51; 95% CI, 0.33-0.77; P<0.001).

CONCLUSIONS

In JUPITER, cholesterol efflux capacity was associated with incident CVD in individuals on potent statin therapy but not at baseline. For both baseline and on-statin analyses, HDL particle number was the strongest of 4 HDL-related biomarkers as an inverse predictor of incident events and biomarker of residual risk.

CLINICAL TRIAL REGISTRATION

URL: http://www.clinicaltrials.gov. Unique identifier: NCT00239681.

Tests of calibration and goodness-of-fit in the survival setting

To access the calibration of a predictive model in a survival analysis setting, several authors have extended the Hosmer-Lemeshow goodness-of-fit test to survival data. Grønnesby and Borgan developed a test under the proportional hazards assumption, and Nam and D'Agostino developed a nonparametric test that is applicable in a more general survival setting for data with limited censoring. We analyze the performance of the two tests and show that the Grønnesby-Borgan test attains appropriate size in a variety of settings, whereas the Nam-D'Agostino method has a higher than nominal Type 1 error when there is more than trivial censoring. Both tests are sensitive to small cell sizes. We develop a modification of the Nam-D'Agostino test to allow for higher censoring rates. We show that this modified Nam-D'Agostino test has appropriate control of Type 1 error and comparable power to the Grønnesby-Borgan test and is applicable to settings other than proportional hazards. We also discuss the application to small cell sizes.

Impact of correlation on predictive ability of biomarkers

In this paper, we investigate how the correlation structure of independent variables affects the discrimination of risk prediction model. Using multivariate normal data and binary outcome, we prove that zero correlation among predictors is often detrimental for discrimination in a risk prediction model and negatively correlated predictors with positive effect sizes are beneficial. A very high multiple R-squared from regressing the new predictor on the old ones can also be beneficial. As a practical guide to new variable selection, we recommend to select predictors that have negative correlation with the risk score based on the existing variables. This step is easy to implement even when the number of new predictors is large. We illustrate our results by using real-life Framingham data suggesting that the conclusions hold outside of normality. The findings presented in this paper might be useful for preliminary selection of potentially important predictors, especially is situations where the number of predictors is large.

Misuse of DeLong test to compare AUCs for nested models

The area under the receiver operating characteristics curve (AUC of ROC) is a widely used measure of discrimination in risk prediction models. Routinely, the Mann-Whitney statistics is used as an estimator of AUC, while the change in AUC is tested by the DeLong test. However, very often, in settings where the model is developed and tested on the same dataset, the added predictor is statistically significantly associated with the outcome but fails to produce a significant improvement in the AUC. No conclusive resolution exists to explain this finding. In this paper, we will show that the reason lies in the inappropriate application of the DeLong test in the setting of nested models. Using numerical simulations and a theoretical argument based on generalized U-statistics, we show that if the added predictor is not statistically significantly associated with the outcome, the null distribution is non-normal, contrary to the assumption of DeLong test. Our simulations of different scenarios show that the loss of power because of such a misuse of the DeLong test leads to a conservative test for small and moderate effect sizes. This problem does not exist in cases of predictors that are associated with the outcome and for non-nested models. We suggest that for nested models, only the test of association be performed for the new predictors, and if the result is significant, change in AUC be estimated with an appropriate confidence interval, which can be based on the DeLong approach.

Novel metrics for evaluating improvement in discrimination: net reclassification and integrated discrimination improvement for normal variables and nested models

Net reclassification and integrated discrimination improvements have been proposed as alternatives to the increase in the area under the curve for evaluating improvement in the performance of risk assessment algorithms introduced by the addition of new phenotypic or genetic markers. In this paper, we demonstrate that in the setting of linear discriminant analysis, under the assumptions of multivariate normality, all three measures can be presented as functions of the squared Mahalanobis distance. This relationship affords an interpretation of the magnitude of these measures in the familiar language of effect size for uncorrelated variables. Furthermore, it allows us to conclude that net reclassification improvement can be viewed as a universal measure of effect size. Our theoretical developments are illustrated with an example based on the Framingham Heart Study risk assessment model for high-risk men in primary prevention of cardiovascular disease.

Equivalence of improvement in area under ROC curve and linear discriminant analysis coefficient under assumption of normality

In this paper we investigate the addition of new variables to an existing risk prediction model and the subsequent impact on discrimination quantified by the area under the receiver operating characteristics curve (AUC of ROC). Based on practical experience, concerns have emerged that the significance of association of the variable under study with the outcome in the risk model does not correspond to the significance of the change in AUC: that is, often the variable is significant, but the change in AUC is not. This paper demonstrates that under the assumption of multivariate normality and employing linear discriminant analysis (LDA) to construct the risk prediction tool, statistical significance of the new predictor(s) is equivalent to the statistical significance of the increase in AUC. Under these assumptions the result extends asymptotically to logistic regression. We further show that equality of variance-covariance matrices of predictors within cases and non-cases is not necessary when LDA is used. However, our practical example from the Framingham Heart Study data suggests that the finding might be sensitive to the assumption of normality.

The effects of co-morbidity on the onset and persistence of generalized anxiety disorder in the ICPE surveys. International Consortium in Psychiatric Epidemiology

BACKGROUND

Although it is well known that generalized anxiety disorder (GAD) is highly co-morbid with other mental disorders, little is known about the extent to which earlier disorders predict the subsequent first onset and persistence of GAD. These associations are examined in the current report using data from four community surveys in the World Health Organization (WHO) International Consortium in Psychiatric Epidemiology (ICPE).

METHOD

The surveys come from Brazil, Canada, the Netherlands and the United States. The Composite International Diagnostic Interview (CIDI) was used to assess DSM-III-R anxiety, mood and substance use disorders in these surveys. Discrete-time survival analysis was used to examine the associations of retrospectively reported earlier disorders with first onset of GAD. Logistic regression analysis was used to examine the associations of the disorders with persistence of GAD.

RESULTS

Six disorders predict first onset of GAD in all four surveys: agoraphobia, panic disorder, simple phobia, dysthymia, major depression and mania. With the exception of simple phobia, only respondents with active disorders have elevated risk of GAD. In the case of simple phobia, in comparison, respondents with a history of remitted disorder also have consistently elevated risk of GAD. Simple phobia is also the only disorder that predicts the persistence of GAD.

CONCLUSIONS

The causal processes linking temporally primary disorders to onset of GAD are likely to be state-dependent. History of simple phobia might be a GAD risk marker. Further research is needed to explore the mechanisms involved in the relationship between simple phobia and subsequent GAD.