Plasma metabolomic analysis indicates flavonoids and sorbic acid are associated with incident diabetes: A nested case-control study among Women's Interagency HIV Study participants.

INTRODUCTION: Lifestyle improvements are key modifiable risk factors for Type 2 diabetes mellitus (DM) however specific influences of biologically active dietary metabolites remain unclear. Our objective was to compare non-targeted plasma metabolomic profiles of women with versus without confirmed incident DM. We focused on three lipid classes (fatty acyls, prenol lipids, polyketides).
MATERIALS AND METHODS: Fifty DM cases and 100 individually matched control participants (80% with human immunodeficiency virus [HIV]) were enrolled in a case-control study nested within the Women's Interagency HIV Study. Stored blood samples (1-2 years prior to DM diagnosis among cases; at the corresponding timepoint among matched controls) were assayed in triplicate for metabolomics. Time-of-flight liquid chromatography mass spectrometry with dual electrospray ionization modes was utilized. We considered 743 metabolomic features in a two-stage feature selection approach with conditional logistic regression models that accounted for matching strata.
RESULTS: Seven features differed by DM case status (all false discovery rate-adjusted q<0.05). Three flavonoids (two flavanones, one isoflavone) were respectively associated with lower odds of DM (all q<0.05), and sorbic acid was associated with greater odds of DM (all q<0.05).
CONCLUSION: Flavonoids were associated with lower odds of incident DM while sorbic acid was associated with greater odds of incident DM.

Introduction

Diabetes mellitus (DM) is associated with an increasingly heavy burden of disease globally [1,2], including among people with human immunodeficiency virus (HIV) [3,4]. Over the last three decades, the number of people with DM more than doubled from 211 million in 1990 to 476 million in 2017 [1]. This increase largely reflects the growing number of people with Type 2 diabetes mellitus (T2DM), which also accounts for most DM cases [1]. A major obstacle to reducing T2DM incidence, prevalence, and mortality is increasing the effectiveness of prevention strategies, including through an improved understanding of modifiable risk factors [5] in diverse phenotypic subgroups.

Lifestyle modifications, including healthier dietary patterns with more fruits and vegetables and fewer processed foods, are key prevention recommendations for reducing the risk of T2DM [2]. Despite a large literature regarding specific diets [6] and nutrients [7] in association with diabetes outcomes, findings across some previous studies are inconsistent [8]. It remains a challenge to account for the extensive inter- and intra-individual heterogeneity in consumption patterns, nutritional requirements, dietary responses (e.g., nutrient absorption) [9] as well as the roles of non-nutrients and other dietary components [10]. Evaluation of dietary interventions, particularly long-term adherence, is a major obstacle. Circulating biomarkers of dietary intake could circumvent these issues and potentially serve as improved metrics of specific biologically-active metabolites and earlier predictors of long-term metabolic health [11-13].

Metabolomics can provide high-throughput, comprehensive, and relatively non-biased examination of low molecular weight metabolites [14]. Metabolomic data have the potential to characterize overall dietary intake and to identify earlier, modifiable dietary risk factors for DM [14]. Branched-chain amino acids and sphingolipids have been extensively evaluated in the context of insulin resistance and DM [15,16]. In a recent study among Women’s Interagency HIV Study (WIHS) participants, cholesteryl esters, diacylglycerols, lysophosphatidylcholines, phosphatidylcholines, and phosphatidylethanolamines were associated with diabetes risk [17].

This individually matched nested case-control study compared non-targeted plasma metabolomic profiles among women with versus without confirmed, incident DM. We evaluated lipids and lipid classes that represent potential dietary modifiable risk factors of DM. Specifically, our focus was on three classes of lipids (fatty acyls, prenol lipids, polyketides) [18].

Materials and methods

Study participants

WIHS was a multicenter prospective cohort study among U.S. women with HIV and women without HIV who had similar risk behaviors as HIV-seropositive women [19,20]. WIHS merged with the Multicenter AIDS Cohort Study (MACS) in 2019 to form the MACS/WIHS Combined Cohort Study [21]. In WIHS, HIV-seronegative women were enrolled based upon having similar risk behaviors as HIV-seropositive women [19,20]. This study included data collected from 3,772 women enrolled at six WIHS consortia (Bronx/Manhattan, NY; Brooklyn, NY; Los Angeles/Southern California/Hawaii; San Francisco/Bay Area, CA; Chicago, IL; Washington, DC) [19]. This nested-case control study included 50 cases and 100 matched controls in the final analytic dataset (S1 Fig).

Data collection

As part of the parent cohort study, participants completed in study visits every six months from October 2000 to April 2008. At baseline and at each semi-annual follow-up visit, women completed questionnaires regarding self-reported sociodemographics, behavioral risk and lifestyle factors. During study visits, trained study staff conducted interviews of medical history including antiretroviral treatment history, and performed physical examinations (e.g., anthropometry) and phlebotomy.

Case (incident diabetes mellitus) and control definitions

We defined women as cases with incident, confirmed DM if they met any of the following criteria: a) ≥ two fasting blood glucose (FBG) ≥126 mg/dL; b) one FBG ≥ 126 mg/dL and one random blood glucose (RBG) ≥ 200 mg/dL; c) one FBG ≥ 126 mg/dL and self-reported DM medications (S1 Table). For each case, the index visit (visit 0) was the visit of DM diagnosis. If participants had two FBG measurements, visit 0 was considered the first date of DM presentation (i.e., first of two DM measurements). All FBG concentrations prior to the index visit were <126 mg/dL. Semiannual visits immediately preceding visit 0 were denoted by the corresponding negative study visit number (e.g., -1 for six months prior, -2 for 12 months prior). We assayed a single stored plasma sample from a study visit between one to two years before the index visit of each case.

We matched every DM case to two controls based on blood glucose, HIV serostatus, use of antiretroviral therapy, race and ethnicity, age ± 15 years, and availability of stored blood sample. To control for metabolic parameters potentially associated with impaired fasting glucose, the first control (“FBG-matched control”) was matched on the case’s FBG ± 10 mg/dL at the same calendar period visit that their corresponding case had an available stored plasma sample. The second control (“normoglycemic control”) had all prior longitudinal glucose values <100 mg/dL and was selected without matching by FBG at the same visit as their corresponding case; this control had a plasma sample available at the same calendar period visit as the case.

Glucose assays

Fasting blood samples were assayed for glucose concentrations by hexokinase assay (Olympus 5200, 5400 and AU600 automated instruments; Olympus America, Inc., Melville, NY), as previously detailed [22].

Metabolomic profiling

Plasma samples were collected in sodium citrate (CPT) vacutainers, centrifuged, and stored at -80°C until thawed for non-targeted metabolomic assays. Plasma samples were randomly sorted by matching strata (DM case, FBG-matched and normoglycemic control) into three sets. Samples in each set were assayed for metabolomic data in a separate run; these three batches are subsequently referred to as WIHS1-3. All sample processing and metabolomic assays were conducted by laboratory technicians blinded to the case or control status of each sample. Initial sample processing to extract metabolites followed the same protocol, which has been previously detailed [23]. Standard operating procedures and quality assurance/quality control of metabolomic assays have also been described [24].

Liquid chromatography-mass spectrometry

Plasma samples were assayed in triplicate for metabolomic profiles by time-of-flight liquid chromatography mass spectrometry (LC-MS; Model 6250; Agilent Technologies, Santa Clara, CA) with dual electrospray ionization (ESI) modes [24]. Analytes were separated by C18-based reverse phase column (2 mm x 150 mm Zorbax SB Aq 3.5 um column) in positive and negative ESI modes, which enables greater coverage of features [25]. LC parameters included: autosampler temperature 4°C, 5 μL injection volume, column temperature 55°C, and flow rate 0.4 ml/L. The linear gradient was 2–98% of 0.2% (v/v) acetic acid in water (solvent A) to 0.2% (v/v) acetic acid in methanol over 15 min, followed by 2 min hold of solvent B and 5 min post-time. ESI settings included: capillary voltage (Vcap) at 4000 V for positive ion mode and 3500 V for negative ion mode, fragmentor voltage at 135 V, liquid nebulizer at 45 psi, N2 drying gas at 12 L/min and 250°C. Data were acquired by Agilent MassHunter Qual Workstation Data Acquisition software with the following settings: rate 2.5 spectra/s, centroid mode, and mass scan range 15–2250 [26].

Metabolomic data extraction and preprocessing

Each metabolomic feature was defined by a unique mass-to-charge ratio (m/z) and retention time (RT) combination; relative abundance of feature ion intensities were reported as peak areas. An internal reference standard mix included six standard masses ranging from 112.985587 to 1633.949753; this was utilized for mass axis calibration, error assessments and corrections. Major pre-processing steps included: feature detection and extraction; correlation (co-varying ions within each chromatogram); accounting for adducts, isomers, and fragments.

In terms of data-filtering, metabolomic features with ion counts in >80% across participant samples in each data subset (by assay batch [WIHS1-3] and ESI mode [+, -]) were retained for analysis [27]. Missing relative abundance values (e.g. ≤1) were set to the limit of detection (LOD)/2. All feature ion counts were log2 normalized prior to analysis.

Statistical and bioinformatic analysis

Analysis was conducted utilizing R (version 4.0.3; R Foundation for Statistical Computing; Vienna, Austria), including MetaboAnalystR [28], and SAS (version 9.4; SAS Institute Inc.; Cary, NC, US). Statistical significance was based on two-sided hypothesis tests, and α < 0.05. We initially screened metabolomic features with feature-by-feature unadjusted regressions (Stage 0); since this was a screening criterion, features remained eligible with a p<0.05 that was not false discovery rate adjusted. Subsequently, eligible features were evaluated in feature-by-feature adjusted regressions with metabolomic data (Stage 1); false discovery rate (FDR) adjusted q-value <0.05 was considered significant (S2 Fig). We used a complete-case approach for all key variables aside from metabolomic data (S1 Fig).

Descriptive analysis and visualizations

Continuous and categorical variables were summarized as medians (interquartile ranges [IQR]) or N’s (percentages). Metabolomic features (i.e., log2 relative abundance) were compared across subgroups by non-parametric test statistics (e.g. Kruskal-Wallis). Log2-normalized feature relative abundances and clinical indicators were evaluated by Spearman rank-order correlation coefficients. We visually compared differences of log2-normalized feature relative abundances between the three case-control groups via unsupervised dimensionality reduction (principal components analysis [PCA]), supervised discriminant analysis approaches (e.g. partial least squares discriminant analysis [PLS-DA], orthogonal PLS-DA [OPLS-DA]), and hierarchical clustering in heatmaps. Heatmaps were based on calculated Euclidean distances as the similarity index with Ward’s linkage as the agglomeration method (clustering based on minimizing sum of squares between any two clusters). We considered permutation test statistics for PLS-DA due to potential overfitting issues.

Metabolomic feature selection approach

We utilized a two-stage metabolomic feature selection approach to evaluate the associations between features and case-control status in each data subset (by assay batch [WIHS1-3] and ESI mode [+, -]; (S2 Fig). All conditional logistic models considered a binary categorization of DM cases versus both controls as the primary dependent variable of interest and accounted for matching strata, which reflect individual-matching by blood glucose (FBG-matched, normoglycemic), HIV serostatus, use of antiretroviral therapy, race and ethnicity, age ± 15 years, and availability of stored blood sample. In Stage 0 screening, unadjusted conditional logistic regressions models assessed the associations between case-control status and log2 feature relative abundance. Metabolomic features differing across groups (p<0.05) were considered eligible for Stage 1 regression models.

In Stage 1, multivariable conditional logistic regressions evaluated associations between case-control status and log2 feature relative abundance while accounting for the matching strata and additional covariates. The model equation was: where p = probability of DM case study group, and z = stratum indicator variables (Eq (1)). Metabolomic features were considered associated with the study group (DM cases vs controls) across groups based on β1 (FDR-adjusted q<0.05). We only reported Stage 1 results from three lipid classes of interest (fatty acyls, prenol lipids, polyketides), in light of recent lipidomics studies focusing on other lipids classes.

Feature annotations

The putative chemical compound identities of metabolomic features were annotated by comparison with lipids curated from METLIN [29]. Annotations were based on monoisotopic accurate mass match (within ± 10−5). Selected feature annotations were subsequently manually cross-referenced with Lipid Maps [30] and Human Metabolome Database reference database information [31]. We evaluated feature annotation confidence according to the multi-level system proposed by the Schymanski et al [32], which was based on the Metabolomics Standards Initiative (MSI) scoring [33]. Annotations of selected metabolomic features (from adjusted regressions) were considered Levels 2 or 3 [33].

Ethical conduct of research

The Institutional Review Boards (IRBs) at each WIHS site approved of the study protocol and consent forms (IRB approval numbers: Georgetown University #1993–077, Johns Hopkins University H.34.97.05.19.A2, Montefiore Medical Center #03-07-174, Rush University #13–184, State University of New York Downstate Health Sciences University #266921–64, University of California, San Francisco #21–33925, University of Southern California # HS-21-00496). All study participants provided written informed consent in English or Spanish prior to voluntary enrollment and data collection.

Results

One-hundred and fifty women met the inclusion and exclusion criteria and were included in the final analytic dataset. Among these participants, 50 had DM, 50 were FBG-matched controls, and 50 were normoglycemic controls (S1 Fig). Ages ranged from 19 to 62 years at the index study visit; across the three case-control groups, median age ranged from 42 (IQR 36, 48) to 43 (IQR 38, 48; Table 1). In all case-control groups, 80.0% of women had HIV infection (Table 1). Comparing women with HIV infection across the three case-control groups, CD4 cell counts (p = 0.93) and the proportions of women with HIV RNA <400 copies/mL (p = 0.79) were similar (Table 1). Percentages of women on combination antiretroviral therapy (cART), protease inhibitors, stavudine, zidovudine were similar across the three subgroups (all p>0.05; Table 1). Family history of DM was highest among women with DM (61.0%), compared to those in the control subgroups (FBG-matched 28.6%; normoglycemic 43.2%; p = 0.01; Table 1). Median BMI (p = 0.02) and waist circumference (p<0.01) differed across the 3 subgroups (Table 1). Women with DM had the highest median BMI (29.7 kg/m2 [IQR 27.6, 36.5]) and waist circumference (97.4 cm [90.1, 106.5]), compared to the control subgroups (Table 1).

Table 1

Sociodemographic, clinical, and anthropometric indicators among WIHS participants .

	DM cases(n = 50)	FBG-matched controls(n = 50)	Normoglycemic controls(n = 50)	p b
Sociodemographic	Median (IQR) or n (%)
Age (years)	43.3 (37.5, 47.9)	42.7 (36.6, 46.4)	41.8 (35.8, 48.0)	0.66 b
Race
White	12 (24.0)	12 (24.0)	12 (24.0)	1.00 d
Black	31 (62.0)	31 (62.0)	31 (62.0)
Other	7 (14.0)	7 (14.0)	7 (14.0)
Clinical
HIV infection	40 (80.0)	40 (80.0)	40 (80.0)	1.00 d
HIV RNA < 400 copies/ml e	18 (45.0)	16 (40.0)	15 (37.5)	0.79 d
CD4 cell count (cells/mm3) e	476.0 (230.5, 610.0)	465.5 (238.0, 729.0)	387.5 (248.5, 646.5)	0.93 c
cART e	19 (47.5)	23 (57.5)	22 (55.0)	0.65 d
Protease inhibitor e	10 (25.0)	8 (20.0)	11 (27.5)	0.72 d
Stavudine e	8 (20.0)	8 (20.0)	7 (17.5)	0.95 d
Zidovudine e	12 (30.0)	13 (32.5)	11 (27.5)	0.89 d
Total # of visits on NRTI e, f	7.5 (1.5, 11.0)	8.5 (1.0, 11.5)	6.0 (1.0, 11.0)	0.96 c
Family history of DM g	25 (61.0)	12 (28.6)	19 (43.2)	0.01 d
FBG (mg/dL)	92.0 (89.0, 104.0)	93.5 (85.0, 100.0)	81.0 (76.0, 86.0)	<0.01c
HCV infection	17 (34.0)	13 (26.0)	13 (26.0)	0.60 d
Anthropometric
BMI (kg/m2) g	29.7 (27.6, 36.5)	28.4 (23.8, 33.5)	26.0 (22.4, 31.7)	0.02 c
Waist circumference (cm) g	97.4 (90.1, 106.5)	92.4 (82.4, 102.4)	85.8 (78.7, 98.7)	<0.01 c

a At study visit 0 (date of DM diagnosis of cases, and corresponding date of controls in each matching stratum) unless stated otherwise.

b Subgroup comparisons based on one-way ANOVA test statistic among continuous variables with normal distribution (Shapiro-Wilk, p>0.05).

c Kruskal-Wallis test statistic among non-normally distributed continuous variables (Shapiro-Wilk, p≤0.05).

d Likelihood ratio chi-square test statistic among categorical variables.

e Only among women with HIV.

f Total number of visits from study inception to index visit.

g The following covariates were missing among the specified number of participants: Family history of DM (n = 9 cases, n = 8 FBG-matched controls, n = 6 normoglycemic controls), BMI (n = 1 case, n = 2 normoglycemic controls), waist circumference (n = 8 cases, n = 13 FBG-matched controls, n = 7 normoglycemic controls).

Abbreviations: BMI, body mass index; cART, combination antiretroviral therapy; DM, diabetes mellitus; FBG, fasting blood glucose; HIV, human immunodeficiency virus; NRTI, nucleoside reverse transcriptase inhibitor; SD, standard deviation.

Comparing relative abundance of metabolomic features by diabetes case and controls status

After data-filtering, 743 metabolomic features remained (S1 and S3 Figs). Stratifying by the six data subsets (based on assay batch [WIHS1-3] and ESI mode [+, -]), the number of remaining metabolomic features ranged between 23 and 273 (S1 and S3 Figs). Considering these metabolomic features in a hierarchical clustering heatmap, the similarity indices (Euclidean distances) appeared distinct across the three case-control groups (WIHS1 participants, positive ESI mode; Fig 1A). Visualizing metabolomic features in each data subset, unsupervised (PCA) and supervised (OPLS-DA) approaches showed similar clustering across the three case-control groups (S4 and S5 Figs). Fig 1B shows the first three components from PLS-DA of metabolomic features among WIHS1 participants (positive ESI mode; permutation test statistic p>0.05).

Fig 1

Comparing metabolomic profiles by DM case and control (FBG-matched, normoglycemic) groups among WIHS 1 participants (n = 51), based on data from C18 (positive ESI).

A: Hierarchical clustering heatmap was based on calculated Euclidean distances as the similarity index with Ward’s linkage as the agglomeration method (clustering based on minimizing sum of squares between any two clusters). Log2-normalized relative abundance of metabolomic features are represented in rows; study groups of participants are indicated in columns. DM cases are indicated in red (n = 17), FBG-matched controls in green (n = 17), and normoglycemic controls in blue (n = 17). B: Supervised dimensionality reduction was conducted by PLS-DA, in order to visualize clustering across metabolomic features. Study groups are represented as Δ (DM cases), + (FBG-matched controls), and X (normoglycemic controls). Abbreviations: DM, diabetes mellitus; ESI, electrospray ionization; FBG, fasting blood glucose; PLS-DA, partial least squares discriminant analysis; WIHS, Women’s Interagency HIV Study.

Table 2 summarizes associations between metabolomic features and case-control status (DM cases versus controls), based on unadjusted logistic regressions (Stage 0) with conditional likelihood, stratified by data subset. In WIHS1, three metabolomic features (0 in positive ESI mode; 3 in negative ESI mode) were associated with case-control status (all p<0.05). In WIHS2, seven metabolomic features (2 in positive ESI mode; 5 in negative ESI mode) were associated with case-control status (all p<0.05). In WIHS3, 14 metabolomic features (13 in positive ESI mode; 1 in negative ESI mode) were associated with case-control status (all p<0.05).

Table 2

Summary of features differing across DM case and control groups.

		DM case, FBG-matched and normoglycemic controls(# of differing features)						Regressions Details
WIHS discovery, validation sets		WIHS1		WIHS2		WIHS3
Analytical columns (ESI mode)		+	-	+	-	+	-
N (# of participants)		51	48	48	42	48	51	---	---
Feature selection c								Type	Model equation and details
Nf b		45	59	273	122	221	23
Stage 0	p<0.05	0	3	2	5	13	1	Unadjusted regressions	Conditional logistic regression: log (p DM case / (1-p DM case)) = α1 + α2z2 + ⋯ + αSzS + β0 + β1X1 (log2 feature relative abundance), where p = probability of DM case study group, and z = stratum indicator variables
Stage 1	p<0.05	0	0	2	5	8	1	Adjusted regressions; among features associated with study group (p<0.05) in unadjusted regressions	Conditional logistic regression:: log (p DM case / (1-p DM case)) = α1 + α2z2 + ⋯ + αSzS + β0 + β1X1 (log2 feature relative abundance) + β2X2 (BMI) + β3X3 (age [years]), where p = probability of DM case study group, and z = stratum indicator variables
	q<0.05	0	0	2	5	0	N/A

Abbreviations: BMI, body mass index; DM, diabetes mellitus; ESI, electrospray ionization; FBG, fasting blood glucose; WIHS, Women’s Interagency HIV Study.

a Values in this table indicate the number of metabolomic features with log2 relative abundance values, which differed by DM case or control (FBG-matched, normoglycemic) group status.

b After data filtering, the total number of features considered in each data subset are in S1 Fig. These features were considered via the feature selection approach.

Adjusted associations between metabolomic features and diabetogenic subgroups

In conditional multivariable logistic regressions (Stage 1), 7 metabolomic features were respectively associated with case-control status, accounting for matching strata, BMI, and age (all FDR-adjusted q<0.05; Table 2). Per unit increase, two fatty acyls, 6-methyloctan-3one (adjusted odds ratio [aOR] 1.5 [95% CI 1.0, 2.1]; q = 0.04) and sorbic acid (aOR 2.8 [95% CI 1.1, 7.2]; q = 0.04) were associated with elevated odds of diabetes (Table 3). Per unit increases, four polyketides were respectively associated with odds of diabetes, specifically including heteroflavanone C (aOR 0.1 [95% CI <0.1, 0.8); q = 0.04), rotenonic acid (aOR 0.1 [95% CI <0.1, 0.8); q = 0.04), louisfieserone A (0.2 [95% CI <0.1, 0.8); q = 0.04), and (E)-4-nitrostilbene (aOR 1.5 [95% CI 1.0, 2.4]; q = 0.04; Table 3). Podocarpic acid was associated with increased odds of diabetes (aOR 7.1 [95% CI 1.5, 33.4]; q = 0.02; Table 3). Relative abundance of podocarpic acid was compared by case-control status (Fig 2). Data subsets (assay batch [WIHS1-3], ESI mode [+, -]) are specified in Tables 2 and 3.

Table 3

Associations between selected features and study groups (DM cases versus controls).

Lipid category a	WIHS data subset b	Log2 feature (relative abundance)		Unadjusted c			Adjusted d				Lipid Maps ID
		Variable	Chemical Compound	OR	95% CI	P e	aOR	95% CI	p e	FDR-adjusted q f
Fatty acyls	WIHS1 -		Aminocaproic acid	4.3	1.2, 15.4	0.03	2.7	0.6, 13.0	0.20	0.20	LMFA01100035
	WIHS2 -		6-Methyloctan-3-one	1.4	1.0, 2.0	<0.05	1.5	1.0, 2.1	0.04	0.04	LMFA12000129
			Sorbic acid	2.8	1.1, 7.1	0.03	2.8	1.1, 7.2	0.04	0.04	LMFA01030100
	WIHS3 +		3-Oxo-4-methyl-pentanoic acid	0.6	0.4, 0.9	0.02	0.6	0.4, 0.9	0.03	0.07	LMFA01020276
			5,11-Dodecadiynoic acid	0.5	0.3, <1.0	<0.05	0.5	0.2, <1.0	0.04	0.07	LMFA01030464
			10,12-Tetradecadiene-4,6-diynoic acid, (E,E)-	0.6	0.4, 0.9	0.02	0.5	0.3, 0.9	0.03	0.07	LMFA01030583
Polyketides	WIHS1 -		Isosativan	3	1.1, 8.4	0.04	--- g	--- g	--- g	---	LMPK12080030
	WIHS2 +		(E)-4-Nitrostilbene	2	1.1, 3.6	0.03	1.5	1.0, 2.4	0.04	0.04	LMPK13090020
	WIHS2 -		Heteroflavanone C	0.1	<0.1, 0.7	0.02	0.1	<0.1, 0.8	0.03	0.04	LMPK12140478
			Rotenonic Acid	0.1	<0.1, 0.7	0.02	0.1	<0.1, 0.8	0.02	0.04	LMPK12060018
			Louisfieserone A	0.2	<0.1, 0.8	0.02	0.2	<0.1, 0.8	0.03	0.04	LMPK12140697
Prenol Lipids	WIHS2 +		Podocarpic acid	7	1.5, 23.7	0.01	7.1	1.5, 33.4	0.01	0.02	LMPR0104120002
	WIHS3 +		Etretinate	0.2	0.1, 0.9	0.04	0.2	0.1, <1.0	0.04	0.07	LMPR01090046

a Lipid categorization per Lipid Maps classification [30]. Features were selected if: 1) associated with case-control status in unadjusted models (p<0.05); and 2) with annotations in lipid classes of interest (fatty acyls, polyketides, prenol lipids).

b Data subsets based on metabolomic assay run (WIHS sets 1–3) and ESI mode (+, -).

c Unadjusted conditional logistic regression model equation: log (p DM case / (1-p DM case)) = α1 + α2z2 + ⋯ + αSzS + β0 + β1X1 (log2 feature relative abundance), where p = probability of DM case study group, and z = stratum indicator variables.

d Adjusted conditional logistic regression model equation: log (p DM case / (1-p DM case)) = α1 + α2z2 + ⋯ + αSzS + β0 + β1X1 (log2 feature relative abundance) + β2X2 (BMI) + β3X3 (age [years]), where p = probability of DM case study group, and z = stratum indicator variables.

e P value based on Wald chi-square statistic.

f Post-hoc FDR adjustment among each data subset (e.g., WIHS1 +) of features evaluated in Stage 1 regressions and with annotations in lipid classes of interest.

g Results not reported due to model instability.

Abbreviations: aOR, adjusted odds ratio; DM, diabetes mellitus; ESI, electrospray ionization; OR, odds ratio; WIHS, Women’s Interagency HIV Study.

Fig 2

Boxplots of selected features (relative abundances), stratified by DM case and control groups a.

Data subset (e.g. WIHS1 +) specified in Table 3. Abbreviations: DM, diabetes mellitus; FBG, fasting blood glucose.

Discussion

A total of 743 metabolomic features were observed among participants with DM and their controls matched by blood glucose (FBG-matched, normoglycemic), HIV serostatus, use of antiretroviral therapy, race and ethnicity, age ± 15 years, and availability of stored blood sample. Overall, seven features were significantly associated with odds of DM incidence, accounting for matching strata and after FDR adjustment (all q<0.05). Three flavonoids were associated with lower odds of DM incidence, and sorbic acid was associated with greater odds of DM incidence. Our results indicate the need for confirmation of flavonoids, sorbic acid, and their related metabolites via targeted validation with absolute quantitation and mechanistic studies to elucidate their potential respective influences on DM risk.

Protective effects of flavonoids in diabetes

Phytochemicals synthesized by plants and ubiquitous in the human diet, including many flavonoids [34], are hypothesized to be protective against insulin resistance [35] and DM [36], as well as modulate glucose metabolism [37,38]. Our finding that three flavonoids were associated with lower odds of DM is consistent with the directionality of associations found in previous studies [36,39], though our exposure assessment was based on circulating metabolites which differs from dietary intake in other studies. In a meta-analysis including 284,806 participants, dietary intake of total flavonoids was associated with lower risk of T2DM [36]. High dietary intake of flavonoids [39] and adherence to plant-based dietary patterns [40] have also been associated with reduced T2DM risk. Prior studies have suggested potential mechanisms to explain this association, including the ability of some individual flavonoids to inhibit oxidative stress [41] and glycogen phosphorylase, which is a primary enzymatic regulator of glucose and glycogen homeostasis [37]. More broadly, polyphenols have been found to affect glucose and insulin metabolism [42], as well as inhibit glycation and advanced glycation end products production [43].

Previous studies have reported mixed associations, including null results, between diabetogenic indicators and dietary supplementation of isoflavones [44,45]. We found that a circulating isoflavan (isosativan) was associated with greater odds of DM, which contrasts with the null or protective associations observed in other observational studies of dietary isoflavonoid intake on DM-related biomarkers [35,45,46]. These inconsistent findings are potentially explained by the unclear mechanisms linking isoflavonoids and DM, which could include mediators and covariates that need to be accounted for (e.g., extensive heterogeneity of DM pathophysiology, observed pleiotropic influences and differing bioavailabilities of isoflavonoids) [34,35,45].

Elucidating sorbic acid in diabetes

Sorbic acid, or sorbate, is a common synthetic food preservative and metabolite of potassium sorbate, which is a food and pharmaceutical additive [47]. Our finding that sorbate was associated with greater odds of DM is consistent with preliminary evidence of potential explanatory mechanisms [47,48]. Potassium sorbate is completely absorbed after oral ingestion and has cytotoxic and genotoxic influences, which could contribute to elevated risk of a diabetogenic state [47]. Preliminary mechanistic evidence has also shown sorbate to be linked with dysregulated hepatic fatty acid metabolism [48]. Sorbate has also been hypothesized to be an upstream substrate of AGEs [47], which upregulate inflammation and oxidative stress [49] and potentially function as endocrine disrupting chemicals [50]. Future directions of research could examine the: specific metabolic pathways by which sorbic acid and other sorbate additives (e.g., calcium sorbate, potassium sorbate) and other food additives might affect long-term risk of DM incidence, as well as influences of frequency, quantity, timing, and types of sorbates consumed over the human life course on metabolic health.

Strengths and limitations

A major strength of this study was the nested case-control design within a large ongoing prospective cohort study with standardized protocols [19,20]. Specifically, the study design included the confirmation of each participant with incident DM diagnosis after the measurement of metabolomic features; selection of two individually matched controls based on clinical and sociodemographic criteria; and comparison of stored blood samples collected at the same earlier study visit within each matching stratum. The broad consideration of metabolomic features from non-targeted profiling provided a relatively non-biased perspective. This approach was advantageous given limited prior literature regarding the specific lipid classes of interest in context of DM. Furthermore, the inclusion of only women was a strength in light of sex-based differences in metabolism and DM [51]. Simply controlling for biological sex as a variable in regression models does not preclude residual confounding from other related factors (e.g., sex hormone differences), since the etiology of many observed sex-linked differences remains incompletely understood [51].

Several limitations should be noted in interpreting results, particularly the modest sample size, inability to determine causal inferences, and single timepoint evaluation of metabolomic data. In the final analysis, we categorized the two control groups into one group, given the sample size per metabolomic assay batch (WIHS1-3). Further validation of metabolites with authentic reference standards and absolute quantification (plasma concentrations) are needed, in order to confirm feature annotations with higher confidence (e.g., Level 1 [32]) and to facilitate comparisons with other populations. We were not able to consider other covariates, such as inflammation, socioeconomic factors, and ART type, and inter-individual variability of gut microbiota [52,53], that potentially influence our associations of interest; future studies should consider these additional covariates. For example, commensal bacteria have been hypothesized to metabolize dietary flavonoids [54] and to be modulated by polyphenols [55] which may subsequently affect metabolic health. Since HIV status was a matching criterion for selecting controls, this study was not designed to evaluate the role of HIV as a comorbidity. However, some flavonoids have antioxidant functions [34] and a recent study demonstrated that two flavonoid glycosides can activate Vδ1+ T cells to suppress HIV-1 [56], emphasizing the need for future studies to consider the associations of individual flavonoids with DM, HIV, and other comorbidities.

Conclusions

In summary, seven plasma metabolomic features differed among women with DM incidence, compared to their matched controls. Three flavonoids were associated with lower odds of DM incidence. Sorbic acid, a common food preservative, was associated with greater odds of DM. Further studies are needed to validate and delineate the underlying mechanisms of flavonoids and food additives as potential modifiable dietary factors associated with DM, which could improve DM prevention efforts.

Inclusion and exclusion criteria for WIHS study participants, and data filtering of metabolomic features.

(TIF)

Click here for additional data file.

Two-stage feature selection approach.

(TIF)

Click here for additional data file.

Proportions of feature peak areas observed across participants, stratified by metabolomic assay batch (WIHS1-3) and analytical column (+, - ESI).

In each of the six data subsets, the final analytic subset of participants was considered those individuals in complete matching strata. Features were included below if remaining after data filtering (observed among ≥80% of participant samples).

(TIF)

Click here for additional data file.

Unsupervised clustering (PCA) of metabolomic features in each data subset (WIHS sets 1–3, positive and negative ESI modes).

(TIF)

Click here for additional data file.

Supervised clustering (OPLS-DA) of metabolomic features in each data subset (WIHS sets 1–3, positive and negative ESI modes).

(TIF)

Click here for additional data file.

Definitions of cases and controls.

(DOCX)

Click here for additional data file.

(DOCX)

Click here for additional data file.

19 May 2022

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Anandh Babu Pon Velayutham

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Thank you for stating in your Funding Statement:

This manuscript was also partially supported by NIDDK K08-DK117064 (J.O.A.).

Please provide an amended statement that declares *all* the funding or sources of support (whether external or internal to your organization) received during this study, as detailed online in our guide for authors at http://journals.plos.org/plosone/s/submit-now.  Please also include the statement “There was no additional external funding received for this study.” in your updated Funding Statement.

Please include your amended Funding Statement within your cover letter. We will change the online submission form on your behalf.

3. Thank you for stating the following financial disclosure:

The WIHS is funded by the National Institute of Allergy and Infectious Diseases (UO1-AI-35004, UO1-AI-31834, UO1-AI-34994, UO1-AI-34989, UO1-AI-34993, and UO1-AI-42590) and by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (UO1-HD-32632). The study is co-funded by the National Cancer Institute, the National Institute on Drug Abuse, and the National Institute on Deafness and Other Communication Disorders. Funding is also provided by the National Center for Research Resources (UCSF-CTSI Grant Number UL1 RR024131). This manuscript was also partially supported by NIDDK K08-DK117064 (J.O.A.).

Please state what role the funders took in the study.  If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

If this statement is not correct you must amend it as needed.

Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Abstract

Introduction:

- What is the possible influence of human immunodeficiency virus (HIV) on non-targeted plasma metabolomic profiles of women with incident DM?

- Please, briefly explain why the three lipid classes (fatty acyls, prenol lipids, polyketides) have been chosen.

Methods:

Briefly describe which methods were used to assess metabolomic profile.

Conclusion:

Avoid expressions such as: “further etiological studies are needed”. Focus on the association of flavonoids with lower odds of incident DM and on the association of sorbic acid with greater odds of incident DM.

Main text:

Introduction:

The introduction is still lacking in substance. There have been many more papers about the association between specific food groups and the protective effects against DM incidence.

Methods

Please briefly cite the quality assurance/quality control of metabolomic assays used.

Discussion

- Lines 307-308: The introductory information about flavonoids is not important at this moment (“Flavonoids are phytochemicals synthesized by plants and ubiquitous in the human diet, particularly from many fruits and vegetables”). Focus on the relation between these compounds and DM.

- Lines 356-328: “These inconsistent findings are potentially explained by the unclear mechanisms linking isoflavonoids and DM, which could include mediators and covariates that need to be accounted for”.

Besides, it is important to quote that this is a case-control study, so it is not possible to establish a cause-effect relationship between these factors.

- Line 340: “Future directions of research” … Such as?

- Line 341: “sorbic acid and other food additives”… Such as?

Reviewer #2: This study compared non-targeted plasma metabolomic profiles of women with versus without confirmed incident Type 2 diabetes mellitus (DM). This study focused on examining three lipid classes (fatty acyl, prenol lipids, polyketides). The results showed that two flavanones and one isoflavone, respectively, were significantly associated with a lower incidence of diabetes, and sorbic acid was associated with a higher incidence of diabetes. The paper is well written, and the results are well-presented and interesting.

However, the below points should be considered.

• The studies involving humans should require an Institutional Review Board approval number).

• In addition, informed consent should be obtained from all subjects participating in the study. "

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

6 Jun 2022

Academic Editor:

A1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Thank you for your comment and feedback.

We have checked the PLOS ONE style templates and finalized our revision materials per the guidelines.

A2. Thank you for stating in your Funding Statement:

This manuscript was also partially supported by NIDDK K08-DK117064 (J.O.A.).

Please provide an amended statement that declares *all* the funding or sources of support (whether external or internal to your organization) received during this study, as detailed online in our guide for authors at http://journals.plos.org/plosone/s/submit-now. Please also include the statement “There was no additional external funding received for this study.” in your updated Funding Statement.

Please include your amended Funding Statement within your cover letter. We will change the online submission form on your behalf.

We have updated the funding statement with the recommended statement regarding external funding. Please see our response to A3 below.

A3. Thank you for stating the following financial disclosure:

The WIHS is funded by the National Institute of Allergy and Infectious Diseases (UO1-AI-35004, UO1-AI-31834, UO1-AI-34994, UO1-AI-34989, UO1-AI-34993, and UO1-AI-42590) and by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (UO1-HD-32632). The study is co-funded by the National Cancer Institute, the National Institute on Drug Abuse, and the National Institute on Deafness and Other Communication Disorders. Funding is also provided by the National Center for Research Resources (UCSF-CTSI Grant Number UL1 RR024131). This manuscript was also partially supported by NIDDK K08-DK117064 (J.O.A.).

Please state what role the funders took in the study. If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

If this statement is not correct you must amend it as needed.

Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf.

We have updated the financial disclosure statement with the suggested sentence.

“The WIHS is funded by the National Institute of Allergy and Infectious Diseases (UO1-AI-35004, UO1-AI-31834, UO1-AI-34994, UO1-AI-34989, UO1-AI-34993, and UO1-AI-42590) and by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (UO1-HD-32632). The study is co-funded by the National Cancer Institute, the National Institute on Drug Abuse, and the National Institute on Deafness and Other Communication Disorders. Funding is also provided by the National Center for Research Resources (UCSF-CTSI Grant Number UL1 RR024131). This manuscript was also partially supported by NIDDK K08-DK117064 (J.O.A.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. There was no additional external funding received for this study.”

B. Reviewer 1

B1. Abstract

Introduction:

- What is the possible influence of human immunodeficiency virus (HIV) on non-targeted plasma metabolomic profiles of women with incident DM?

- Please, briefly explain why the three lipid classes (fatty acyls, prenol lipids, polyketides) have been chosen.

Thank you for your constructive comments and suggestions, particularly for the abstract.

We have revised the abstract, per your comments. Please see our responses below (B2, B3).

We agree that previous evidence has demonstrated links between HIV, antiretroviral therapy, and metabolic abnormalities including diabetes [1-3]. This present study was designed as a nested case-control study with HIV serostatus as a matching criterion, and therefore, we were not able to evaluate the influence of HIV. We have included the need for future studies to evaluate this important research priority in the introduction and discussion (please see text below).

Introduction (Page 4, 26-27): “Diabetes mellitus (DM) is associated with an increasingly heavy burden of disease globally [4, 5], including among people with human immunodeficiency virus (HIV) [6, 7].”

Discussion (Pages 21-22, Lines 372-376): “Since HIV status was a matching criterion for selecting controls, this study was not designed to evaluate the role of HIV as a comorbidity. However, some flavonoids have antioxidant functions [8] and a recent study demonstrated that two flavonoid glycosides can activate Vδ1+ T cells to suppress HIV-1 [54], emphasizing the need for future studies to consider the associations of individual flavonoids with DM, HIV, and other comorbidities.”

Our objective is a separate although complementary question; specifically, we sought to identify low molecular weight chemical compounds associated with the likelihood of diabetes incidence. This was intended to shed light on the key remaining research gaps of: determining specific metabolic pathways that are dysregulated in diabetes pathophysiology; and designing more effective diabetes prevention interventions among people with and without HIV.

We have included the rationale for focusing on three lipid classes in the introduction.

Introduction (Pages 4-5, Lines 47-50): “In a recent study among Women’s Interagency HIV Study (WIHS) participants, cholesteryl esters, diacylglycerols, lysophosphatidylcholines, phosphatidylcholines, and phosphatidylethanolamines were associated with diabetes risk [9].”

Introduction (Page 5, Lines 53-55): “We evaluated lipids and lipid classes that represent potential dietary modifiable risk factors of DM. Specifically, our focus was on three classes of lipids (fatty acyls, prenol lipids, polyketides) [10].”

In order to clearly explain the rationale regarding both of these points, we prefer to discuss these in the introduction and discussion. We did not include these in the abstract as we think it could cause potential confusion if not fully explained.

B2. Methods:

Briefly describe which methods were used to assess metabolomic profile.

We have included further details regarding the metabolomic assays. Our revised text is below.

Abstract, Methods sub-section (Page 3, Lines 11-13): “Stored plasma samples (1-2 years prior to DM diagnosis among cases; at the corresponding timepoint among matched controls) were assayed in triplicate for high-resolution metabolomics. Time-of-flight liquid chromatography mass spectrometry with dual electrospray ionization modes was utilized.”

B3.

Conclusion:

Avoid expressions such as: “further etiological studies are needed”. Focus on the association of flavonoids with lower odds of incident DM and on the association of sorbic acid with greater odds of incident DM.

We have removed the clause with “further etiological studies are needed” in order to focus the conclusion on key findings from this study (revised text below).

Abstract, Conclusions sub-section (Page 3, Lines 23-24): “Flavonoids were associated with lower odds of incident DM while sorbic acid was associated with greater odds of incident DM.”

B4.

Main text:

Introduction:

The introduction is still lacking in substance. There have been many more papers about the association between specific food groups and the protective effects against DM incidence.

We have updated the introduction, in order to provide more comprehensive context of the previous literature regarding diabetes pathophysiology and the diet. We agree that there is a large literature and therefore cite additional reviews and articles.

Introduction (Page 4, Lines 34-42): “Lifestyle modifications, including healthier dietary patterns with more fruits and vegetables and fewer processed foods, are key prevention recommendations for reducing the risk of T2DM [5]. Despite a large literature regarding specific diets [11] and nutrients [12] in association with diabetes outcomes, findings across some previous studies are inconsistent [13]. It remains a challenge to account for the extensive inter- and intra-individual heterogeneity in consumption patterns, nutritional requirements, dietary responses (e.g., nutrient absorption) [14] as well as the roles of non-nutrients and other dietary components [15]. Evaluation of dietary interventions, particularly long-term adherence, is a major obstacle. Circulating biomarkers of dietary intake could circumvent these issues and potentially serve as improved metrics of specific biologically-active metabolites and earlier predictors of long-term metabolic health [16-18].”

B5.

Methods

Please briefly cite the quality assurance/quality control of metabolomic assays used.

We have included further details and citations for the quality assurance and control standard operating procedures of the metabolomic assays in this study.

Methods (Page 7, Lines 103-107): “All sample processing and metabolomic assays were conducted by laboratory technicians blinded to the case or control status of each sample. Initial sample processing to extract metabolites followed the same protocol, which has been previously detailed [19]. Standard operating procedures and quality assurance/quality control of metabolomic assays have also been described [20].”

B6.

Discussion

- Lines 307-308: The introductory information about flavonoids is not important at this moment (“Flavonoids are phytochemicals synthesized by plants and ubiquitous in the human diet, particularly from many fruits and vegetables”). Focus on the relation between these compounds and DM.

Thank you for this suggestion. In the sub-section titled “Protective effects of flavonoids in diabetes” (pages 19-20), we have revised this text to focus more on the association between flavonoids and diabetes. Examples of edits include deleting the sentence that Reviewer 1 referenced, as well as the following one, and the first sentence of the subsequent paragraph since these all provided a general introduction to flavonoids and isoflavones.

B7.

- Lines 356-328: “These inconsistent findings are potentially explained by the unclear mechanisms linking isoflavonoids and DM, which could include mediators and covariates that need to be accounted for”.

Besides, it is important to quote that this is a case-control study, so it is not possible to establish a cause-effect relationship between these factors.

Thank you for this comment. We agree that case-control studies do not allow for causal inferences. We included this as a limitation and revised the referenced text for greater clarity (please see below).

Discussion (Page 21, Lines 362-363): “Several limitations should be noted in interpreting results, particularly the modest sample size, inability to determine causal inferences, and single timepoint evaluation of metabolomic data.”

Discussion (Page 20, Lines 326-332): “We found that a circulating isoflavan (isosativan) was associated with greater odds of DM, which contrasts with the null or protective associations observed in other observational studies of dietary isoflavonoid intake on DM-related biomarkers [21-23]. These inconsistent findings are potentially explained by the need to account for other key mediators and covariates (e.g., extensive heterogeneity of DM pathophysiology, observed pleiotropic influences and differing bioavailabilities of isoflavonoids) [8, 21, 23].”

B8.

- Line 340: “Future directions of research” … Such as?

We have clarified this sentence. Please see the revised text below, which incorporates further details regarding future related research questions.

Discussion (Page 20, Lines 342-346): “Future directions of research could examine the: specific metabolic pathways by which sorbic acid and other sorbate additives (e.g., calcium sorbate, potassium sorbate) and other food additives might affect long-term risk of DM incidence, as well as influences of frequency, quantity, timing, and types of sorbates consumed over the human life course on metabolic health.”

B9.

- Line 341: “sorbic acid and other food additives”… Such as?

Please see our response in B8.

Reviewer 2:

C1.

This study compared non-targeted plasma metabolomic profiles of women with versus without confirmed incident Type 2 diabetes mellitus (DM). This study focused on examining three lipid classes (fatty acyl, prenol lipids, polyketides). The results showed that two flavanones and one isoflavone, respectively, were significantly associated with a lower incidence of diabetes, and sorbic acid was associated with a higher incidence of diabetes. The paper is well written, and the results are well-presented and interesting.

Thank you very much for your review and suggestions. We have revised the manuscript based on your input.

C2.

However, the below points should be considered.

• The studies involving humans should require an Institutional Review Board approval number).

We have added the Institutional Review Board approval numbers for each of the WIHS clinical sites and the data management center at Johns Hopkins University. Please see the revised text below.

Methods (page 10, lines 188-192): “The Institutional Review Boards (IRBs) at each WIHS site approved of the study protocol and consent forms (IRB approval numbers: Georgetown University #1993-077, Johns Hopkins University H.34.97.05.19.A2, Montefiore Medical Center #03-07-174, Rush University #13-184, State University of New York Downstate Health Sciences University #266921-64, University of California, San Francisco #21-33925, University of Southern California # HS-21-00496). All study participants provided written informed consent in English or Spanish prior to voluntary enrollment and data collection.”

C3.

In addition, informed consent should be obtained from all subjects participating in the study.

We agree. Please find below the revised text in our response to C2, which states this.

References

1. Koethe JR, Lagathu C, Lake JE, Domingo P, Calmy A, Falutz J, et al. HIV and antiretroviral therapy-related fat alterations. Nature Reviews Disease Primers. 2020;6(1):48. doi: 10.1038/s41572-020-0181-1.

2. Nou E, Lo J, Hadigan C, Grinspoon SK. Pathophysiology and management of cardiovascular disease in patients with HIV. The Lancet Diabetes & Endocrinology. 2016;4(7):598-610. doi: 10.1016/S2213-8587(15)00388-5.

3. Monroe AK, Glesby MJ, Brown TT. Diagnosing and Managing Diabetes in HIV-Infected Patients: Current Concepts. Clinical Infectious Diseases. 2014;60(3):453-62. doi: 10.1093/cid/ciu779.

4. Lin X, Xu Y, Pan X, Xu J, Ding Y, Sun X, et al. Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025. Scientific Reports. 2020;10(1):14790. doi: 10.1038/s41598-020-71908-9.

5. World Health Organization. Diabetes. Fact sheet. Geneva: World Health Organization; 2021.

6. American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes—2021. Diabetes Care. 2021;44(Supplement 1):S15. doi: 10.2337/dc21-S002.

7. Monroe AK, Glesby MJ, Brown TT. Diagnosing and managing diabetes in HIV-infected patients: current concepts. Clin Infect Dis. 2015;60(3):453-62. Epub 2014/10/15. doi: 10.1093/cid/ciu779. PubMed PMID: 25313249.

8. Manach C, Scalbert A, Morand C, Rémésy C, Jiménez L. Polyphenols: food sources and bioavailability. The American Journal of Clinical Nutrition. 2004;79(5):727-47. doi: 10.1093/ajcn/79.5.727.

9. Zhang E, Chai JC, Deik AA, Hua S, Sharma A, Schneider MF, et al. Plasma Lipidomic Profiles and Risk of Diabetes: 2 Prospective Cohorts of HIV-Infected and HIV-Uninfected Individuals. The Journal of Clinical Endocrinology & Metabolism. 2021;106(4):999-1010. doi: 10.1210/clinem/dgab011.

10. O'Donnell VB, Dennis EA, Wakelam MJO, Subramaniam S. LIPID MAPS: Serving the next generation of lipid researchers with tools, resources, data, and training. Sci Signal. 2019;12(563). Epub 2019/01/10. doi: 10.1126/scisignal.aaw2964. PubMed PMID: 30622195.

11. Sarsangi P, Salehi-Abargouei A, Ebrahimpour-Koujan S, Esmaillzadeh A. Association between Adherence to the Mediterranean Diet and Risk of Type 2 Diabetes: An Updated Systematic Review and Dose-Response Meta-Analysis of Prospective Cohort Studies. Advances in Nutrition. 2022. doi: 10.1093/advances/nmac046.

12. Zheng Y, Li Y, Qi Q, Hruby A, Manson JE, Willett WC, et al. Cumulative consumption of branched-chain amino acids and incidence of type 2 diabetes. Int J Epidemiol. 2016;45(5):1482-92. Epub 2016/07/13. doi: 10.1093/ije/dyw143. PubMed PMID: 27413102.

13. Mustafa ST, Hofer OJ, Harding JE, Wall CR, Crowther CA. Dietary recommendations for women with gestational diabetes mellitus: a systematic review of clinical practice guidelines. Nutrition Reviews. 2021;79(9):988-1021. doi: 10.1093/nutrit/nuab005.

14. Lampe JW, Navarro SL, Hullar MAJ, Shojaie A. Inter-individual differences in response to dietary intervention: integrating omics platforms towards personalised dietary recommendations. Proc Nutr Soc. 2013;72(2):207-18. Epub 2013/02/06. doi: 10.1017/S0029665113000025. PubMed PMID: 23388096.

15. Yates AA, Dwyer JT, Erdman JW, Jr., King JC, Lyle BJ, Schneeman BO, et al. Perspective: Framework for Developing Recommended Intakes of Bioactive Dietary Substances. Advances in Nutrition. 2021;12(4):1087-99. doi: 10.1093/advances/nmab044.

16. Roberts LD, Koulman A, Griffin JL. Towards metabolic biomarkers of insulin resistance and type 2 diabetes: progress from the metabolome. The Lancet Diabetes & Endocrinology. 2014;2(1):65-75. doi: https://doi.org/10.1016/S2213-8587(13)70143-8.

17. Bhupathiraju SN, Hu FB. One (small) step towards precision nutrition by use of metabolomics. The Lancet Diabetes & Endocrinology. 2017;5(3):154-5. doi: 10.1016/S2213-8587(17)30007-4.

18. Rinschen MM, Ivanisevic J, Giera M, Siuzdak G. Identification of bioactive metabolites using activity metabolomics. Nat Rev Mol Cell Biol. 2019;20(6):353-67. doi: 10.1038/s41580-019-0108-4. PubMed PMID: 30814649.

19. Want EJ, O'Maille G, Smith CA, Brandon TR, Uritboonthai W, Qin C, et al. Solvent-dependent metabolite distribution, clustering, and protein extraction for serum profiling with mass spectrometry. Anal Chem. 2006;78(3):743-52. Epub 2006/02/02. doi: 10.1021/ac051312t. PubMed PMID: 16448047.

20. Lakshmanan V, Rhee KY, Wang W, Yu Y, Khafizov K, Fiser A, et al. Metabolomic Analysis of Patient Plasma Yields Evidence of Plant-Like α-Linolenic Acid Metabolism in Plasmodium falciparum. The Journal of Infectious Diseases. 2012;206(2):238-48. doi: 10.1093/infdis/jis339.

21. Cao H, Ou J, Chen L, Zhang Y, Szkudelski T, Delmas D, et al. Dietary polyphenols and type 2 diabetes: Human Study and Clinical Trial. Critical Reviews in Food Science and Nutrition. 2019;59(20):3371-9. doi: 10.1080/10408398.2018.1492900.

22. Rienks J, Barbaresko J, Oluwagbemigun K, Schmid M, Nöthlings U. Polyphenol exposure and risk of type 2 diabetes: dose-response meta-analyses and systematic review of prospective cohort studies. The American Journal of Clinical Nutrition. 2018;108(1):49-61. doi: 10.1093/ajcn/nqy083.

23. Duru KC, Kovaleva EG, Danilova IG, van der Bijl P, Belousova AV. The potential beneficial role of isoflavones in type 2 diabetes mellitus. Nutrition Research. 2018;59:1-15. doi: https://doi.org/10.1016/j.nutres.2018.06.005.

27 Jun 2022

Plasma metabolomic analysis indicates flavonoids and sorbic acid are associated with incident diabetes: A nested case-control study among Women’s Interagency HIV Study participants

PONE-D-21-36113R1

Dear Dr. Glesby,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Anandh Babu Pon Velayutham

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: The authors have adequately addressed the comments I made in the previous round of review, and I feel the manuscript is now ready for publication

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

30 Jun 2022

PONE-D-21-36113R1

Plasma metabolomic analysis indicates flavonoids and sorbic acid are associated with incident diabetes: A nested case-control study among Women’s Interagency HIV Study participants

Dear Dr. Glesby:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Anandh Babu Pon Velayutham

Academic Editor

PLOS ONE