# Saturated fat from dairy sources and cardio-metabolic health: insights from the STANISLAS cohort

Sandra Wagner, Nicolas Girerd, Claire Lemonnier, Kofivi Yena, Zohra Lamiral, Kévin Duarte, Thomas Merkling, Ludovic Merckle, Erwan Bozec,

Anestis Dougkas, et al.

► To cite this version:

Sandra Wagner, Nicolas Girerd, Claire Lemonnier, Kofivi Yena, Zohra Lamiral, et al. Saturated fat from dairy sources and cardio-metabolic health: insights from the STANISLAS cohort. European Journal of Nutrition, 2025, 64 (6), pp.267. 10.1007/s00394-025-03763-1. hal-05242605

HAL Id: hal-05242605

https://hal.univ-lorraine.fr/hal-05242605v1

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1 Saturated fat from dairy sources and cardio-metabolic health:

2 insights from the STANISLAS cohort

3 Sandra WAGNER¹*, Nicolas GIRERD¹*#, Claire LEMONNIER²†, Kofivi YENA¹†,

4 Zohra LAMIRAL¹, Kevin DUARTE¹, Thomas MERKLING¹, Ludovic MERCKLE¹,

5 Erwan BOZEC¹, Anestis DOUGKAS²,³, Axelle HOGE⁴, Michèle GUILLAUME⁴,

6 Patrick ROSSIGNOL¹,⁵, Julie-Anne NAZARE²,⁶, Cécile VORS²,⁶, Jean-Marc BOIVIN¹,⁷, and

7 Marie-Caroline MICHALSKI²,⁶#

8 ¹ Université de Lorraine, Centre d'Investigation Clinique-Plurithématique Inserm CIC-P 1433,

9 Inserm U1116, CHRU Nancy Brabois, F-CRIN INI-CRCT (Cardiovascular and Renal Clinical
10 Trialists), Nancy, France.

11 ² Université Claude Bernard Lyon1, INRAE, INSERM, CarMeN Laboratory, Pierre-Bénite,
12 France.

13 ³ Institute Lyfe Research Centre, Chateau Du Vivier, BP 25 - 69131 Ecully Cedex, France

14 ⁴ Département des sciences de la santé publique, Université de Liège, Liège, Belgium.

15 ⁵ Medicine and Nephrology-Dialysis Departments, Princess Grace Hospital, and Monaco
16 Private Hemodialysis Centre, Monaco, Monaco

17 ⁶ Centre de Recherche en Nutrition Humaine - Rhône-Alpes, INSERM, INRAE, Université
18 Claude Bernard Lyon1, Hospices Civils de Lyon, Pierre Bénite, France.

19 ⁷ Department of General Medicine, University of Lorraine, Vandoeuvre-lès-Nancy, France.

20 *co-first authors

21 †co-second authors
22 #Correspondence to: Prof. Nicolas Girerd (regarding the STANISLAS cohort) and Dr. Marie-

23 Caroline Michalski (regarding the present study on dairy fat - DAILICATE project)

24 Prof. Nicolas Girerd:

25 Mailing address: Centre d'Investigation Clinique Plurithématique, Pierre Drouin - INSERM -

26 CHRU de Nancy - Université de Lorraine & F-CRIN INI-CRCT (Cardiovascular and Renal

27 Clinical Trialists)

28 Institut Lorrain du Cœur et des Vaisseaux Louis Mathieu, 4 rue du Morvan, 54500 Vandoeuvre-

29 lès-Nancy, France

30 Telephone number: +33 3 83 15 73 22

31 E-mail: n.girerd@chru-nancy.fr

32
33 Dr Marie-Caroline Michalski:
34 Mailing address: Laboratoire CarMeN, Inserm U1060, Bâtiment CENS-ELI 2D, Centre

35 Hospitalier Lyon Sud, 165 chemin du Grand Revoyet, 69310 PIERRE BENITE, France

36 Telephone number: +33 4 26 23 61 71

37 E-mail: Marie-Caroline.Michalski@inrae.fr
38 **Abstract**

39 **Purpose.** The health impact of dairy products is debated due to their high saturated fatty acid 40 (SFA) content. Emerging evidence suggests that dairy products intake may reduce 41 cardiovascular disease risk, despite their SFA-rich lipids, leading to the "dairy food matrix" 42 concept. Nevertheless, the impact of lipid and SFA intake from whole dairy sources on cardio- 43 metabolic health remains poorly understood. This study investigated the cross-sectional 44 association of SFA intake from various types of dairy products and dairy fat sources with 45 cardio-metabolic outcomes.

46 **Methods.** Data from 1619 participants of the STANISLAS cohort, who underwent extensive 47 metabolic and cardiovascular phenotyping and completed a 133-item food frequency 48 questionnaire covering dairy products and other dairy fat sources, were used. Associations of 49 total dietary lipid, total dietary SFA, and SFA intakes from dairy products and dairy fat sources 50 with metabolic outcomes (metabolic syndrome, hyperlipidemia, type 2 diabetes, prediabetes) 51 and subclinical cardiovascular damages (i.e., arterial stiffness, atherosclerosis, left ventricular 52 mass, diastolic dysfunction) were assessed using mixed models.

53 **Results.** Median [interquartile range] lipid consumption via dairy products and dairy fat sources 54 was 23 [15, 34] g/day. Higher total dietary lipid intake was associated with lower arterial 55 stiffness (exp(β) [95%CI]: 0.96 [0.92-0.99]). Total dietary SFAs and SFAs from dairy products 56 were both inversely associated with left ventricular mass (exp(β) [95%CI]: 0.85 [0.74-0.98], 57 and 0.99 [0.99-0.99], respectively). SFAs from dairy fat sources, particularly from Butter, were 58 inversely associated with hyperlipidemia (OR [95%CI]: 0.96 [0.93-0.99] for 59 hypertriglyceridemia, 0.96 (0.93-0.99) for elevated LDLc, 0.95 (0.92-0.98) for elevated non- 60 HDLc, 0.93 (0.89-0.96) for elevated Apolipoprotein B). SFAs from Yogurt was inversely 61 associated with Apolipoprotein B (OR [95%CI]: 0.92 (0.86-0.98)).
62 **Conclusion.** Examining the specific matrix subtypes, our results suggest altogether neutral to
63 beneficial associations of SFAs from dairy products and dairy fat sources with cardio-
64 metabolic health.

65
66 Keywords: dairy matrix, saturated fatty acid, cardiovascular damages, metabolism, food
67 matrix, epidemiology
## 68 Background

Milk and dairy products are a major nutritive food source worldwide, providing essential nutrients such as calcium, magnesium, selenium, riboflavin, vitamins B12 and B5. Milk lipids present a unique richness, including over 400 different fatty acids (including short-, medium-, natural trans-, branched-chain and conjugated species), polar lipids of the milk fat globule membrane, and fat-soluble vitamins [1]. Thus, milk and dairy products play a key role in healthy human nutrition and development throughout life. However, since the 1970s, the health effect of milk and dairy products has been questioned due to their high saturated fatty acid (SFA) content [2, 3]. In the diet, SFAs consist mainly of lauric, myristic and palmitic acids, which are atherogenic when consumed in excess (whereas stearic acid has a neutral effect) [4]. Excessive intake of SFAs has been associated with poorer metabolic health and an increased risk of cardiovascular disease (CVD) [5]. Since 1980, recommendations have advised limiting SFA intake to less than 10% of total calories to reduce CVD risk [6, 7]. Nonetheless, more recent meta-analyses found no evidence that reducing SFA intake may reduce CVD incidence [8–10], whereas others report a mild beneficial effect [11]. These findings challenge the notion that further restricting dietary SFAs will reduce clinical events [12]. Emerging evidence suggests that the effects of SFA intake vary according to food source [12–14], highlighting the so-called “food matrix effect”. The term “food matrix” describes the overall structure of a food, the spatial organization of the nutrients and structures within it, and how these interact with each other; it can induce differential metabolic fate and impacts of nutrients (“food matrix effect”) [15]. For instance, processed red meat contains SFAs, but also high amounts of sodium and other preservatives that may have unfavorable effects on CVD risk factors. Conversely, dairy products (incl. milk, yogurt, cheese), an important source of SFA, also provide beneficial nutrients, including calcium, vitamin D, and proteins [1]. Some dairy products contain probiotics and bacterially-produced bioactive peptides that may also counterbalance the
potentially unfavorable physiological effects of the atherogenic SFAs [12, 16, 17]. In addition, blood lipid responses to milk-derived SFAs have been shown to differ based on food matrix structure. Meta-analyses of randomized controlled dietary intervention trials showed that cheese intake reduced LDLc and HDLc compared to butter [18, 19], whose matrix primarily consists of a fat continuum (“bulk”) and contains only few other nutrients such as proteins and calcium (here in butter, trapped in aqueous droplets within the fat). Recent clinical research has also highlighted the beneficial effects of polar lipids of the milk fat globule membrane on cholesterol metabolism [20, 21], and the mitigating impact of the viscous-to-rigid texture of certain dairy matrices in which the fat droplets/inclusions are embedded such as cheese, on digestion, intestinal absorption and satiety [22, 23]. Moreover, most national dietary guidelines emphasize the consumption of nutrient-dense dairy products such as milk, cheese, and yogurt [24]. However, other sources of dairy fat such as butter and cream are often excluded from this “dairy products” category [15, 24].

Notwithstanding, SFAs are often studied as a whole, and despite these differences, few studies have explored the relationship between food sources of SFAs and cardiometabolic health. In an epidemiological study from the Multi-Ethnic Study of Atherosclerosis, higher SFA intake from dairy products (excluding butter, considered as another SFA source) has been associated with lower CVD risk [13]. Of note, the association between butter SFA intake and CVD risk was neutral in this study: this non-deleterious association may be due to the presence of several favorable bioactive fatty acids in milk fat (e.g. short-chain species that are readily beta-oxidized, trans-vaccenic and trans-palmitoleic acids that have been associated with favorable metabolic effects [1]). However, no studies have thoroughly examined SFA intake stratified according to the various specific types of dairy products and dairy fat sources in this setting.
In light of the above, this study aimed to assess the cross-sectional associations of SFAs from
individual types of milk-related sources (dairy products per se vs. other dairy fat sources), as
well as SFAs from each type of milk-related products (i.e., dairy product categories: Milk,
Yogurt, Unripened Cheese, Ripened Cheese; dairy fat source categories: Cream, Butter, Dairy
desserts) with metabolic outcomes, i.e., pre-diabetes, metabolic syndrome and hyperlipidemia,
as well as subclinical cardiovascular damages, i.e. carotid-femoral pulse wave velocity
(cfPWV), a surrogate marker of arterial stiffness [25], carotid intima-media thickness (cIMT),
a surrogate marker of atherosclerosis [26], left ventricular (LV) mass, and diastolic dysfunction
(DD).

125 Methods

126 Study population

127 The STANISLAS cohort is a population-based study of 1,006 families (4,295 participants) from
128 the Lorraine region in Eastern France recruited between 1993–1995 at the Center for Preventive
129 Medicine. Participants were of French origin, living in the Lorraine region, and free of acute or
130 chronic disease. Participants were followed every 5 to 10 years. From 2011 to 2016, 1,705
131 participants underwent their fourth examination. The STANISLAS study has been extensively
described elsewhere [27].
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134 The present study focused on the participants who underwent their fourth examination, where
135 food data were collected, enabling us to acquire detailed information regarding milk-related
136 lipids. Ten individuals with missing dietary data and 63 participants with daily energy intake
137 either below 1,000 or above 5,000 kcal were excluded from the analysis. The exclusion of
138 participants with daily energy intake below 1,000 kcal or above 5,000 kcal is a common
139 approach in nutritional epidemiology to ensure data reliability and exclude implausible dietary
reports. Thus, 1,632 participants were included in the initial descriptive analyses. Thirteen participants were further excluded from the subsequent analyses due to missing values for at least one of the covariates. To maintain statistical power, the cross-sectional analyses for each outcome were conducted with the maximum number of individuals with available data (Figure 1).

Data collection

Dietary assessment

Dietary intake was assessed using a validated semi-quantitative food frequency questionnaire (FFQ) with 133 items [28]. The participants reported their consumption frequency and portion sizes of 133 food and beverage items over the previous 3 months. Consumption frequency was reported using 6 levels in the FFQ, ranging from “never or rarely” to “2 times or more a day.” The portion size of each food or beverage item was estimated using standard serving sizes and food models. Daily nutrient intakes were calculated in grams per day by multiplying the consumption frequency of each item by the nutrient content of selected portions. Nutritional data were extracted from the French food composition database established by the French Data Center on Food Quality (Ciqual, last updated in 2013). The DASH score was calculated to evaluate global diet quality [29], and the number of servings of ultra-processed foods using the NOVA classification, which regroups the foods into 4 categories according to their level of processing, with ultra-processed food (UPF) defined as the 4th category (NOVA4) [30]. A few dairy product items were classified under the UPF category such as flavored milk and flavored yogurt [31]. Low-fat butter and dairy dessert were also categorized under the UPF category [31].
164 *Types of dietary sources of milk-related lipids/SFAs*

165 We first examined the 133-item FFQ to identify all individual milk-related products that are
166 quantifiable sources of milk lipids (and SFAs therein): this led to a list of 28 different milk-
167 related products (Supplementary Table 1). Subsequent categories were then defined: (i) first
168 globally, namely Dairy products vs. other Dairy fat sources, (ii) then more specifically, namely
169 Milk, Yogurt, Cheese (among which Ripened vs. Unripened), Butter, Cream, and Dairy desserts
170 (Supplementary Table 1). All types of milk and yogurt (skimmed, low-fat, and full-fat) were
171 grouped together under "Milk" and "Yogurt" categories, respectively, without distinction by fat
172 content as SFA intake from the different products was the primary variable of interest (the
173 differential fat/SFA content of skimmed, low-fat and full-fat products being *de facto* taken into
174 account). Lipid and SFA intakes from these defined types of dairy products were calculated by
175 adding the intakes from the corresponding individual products within. Total dietary lipids and
176 SFA intakes were also calculated by adding the intakes from the whole foods from the FFQ.
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178 *Metabolic outcomes*

179 Metabolic syndrome (MetS) was defined according to the National Cholesterol Education
180 Program Adult Treatment Panel 3 (NCEP ATP 3) [32]. ATP 3 is one of the most widely used
181 concepts for assessing MetS. It requires the presence of at least three of the following defined
182 criteria: waist circumference >102 cm in men and >88 cm in women; office blood pressure
183 ≥130/85 mmHg or treated by hypertensive drugs; elevated triglycerides ≥1.5 g/L or treated by
184 lipid-lowering drugs; fasting blood glucose ≥1.10 g/L or treated by antidiabetic drugs; reduced
185 HDL cholesterol <0.4 g/L in men and <0.5 g/L in women.
186

186 Diabetes and prediabetes combined outcome was defined as fasting blood glucose ≥1.10 g/L or
187 treated by antidiabetic drugs. To best account for the diversity of hyperlipidemia types and the
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emergence of new markers of cardiovascular risk such as non-HDL-cholesterol and
Apolipoprotein B (ApoB), four different related outcomes were defined: (i)
hypertriglyceridemia was defined as elevated triglycerides ≥1.5 g/L or treated by lipid-lowering
drugs, (ii) hypercholesterolemia was defined as elevated LDL-cholesterol≥1.6 g/L or treated by
lipid-lowering drugs, (iii) elevated non-HDL-cholesterol as non-HDL-cholesterol ≥1.9 g/L or
treated by lipid-lowering drugs, and (iv) elevated ApoB as ApoB≥1.3 g/L or treated by lipid-
lowering drugs.

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Cardiovascular damage

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Carotid-femoral pulse wave velocity (cfPWV)

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cfPWV was assessed with the Complior® device (ALAM Medical, France), which
simultaneously records arterial pulse waves at carotid and femoral sites, according to the
recommendations of the European Network for Noninvasive Investigation of Large Arteries
[33]. Two sensors were placed simultaneously on the carotid artery and femoral artery. Two
measurements were made, with cfPWV calculated as their mean. If the 2 measurements differed
by >0.5 m/s, a third measurement was recorded, and the cfPWV was then calculated as the
median of the 3 measurements. The onboard foot-to-foot algorithm based on the second-
derivative waveforms was used to determine the transit time. The carotid-to-femoral, carotid-
to-sternal-notch, and sternal-notch-to-carotid distances were measured with a measuring tape,
and cfPWV (m/s) was obtained with the formula cfPWV = D × 0.8/(t).
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## Carotid intima-media thickness (cIMT)

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cIMT measurements were routinely performed by high-resolution echo-tracking in a controlled environment at 22 ±1°C after 10 min of rest in the supine position as described elsewhere [34].

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Carotid diameter, distension, and intima-media thickness were measured on the right common carotid artery. Four measurements were obtained per patient. Examinations were performed using the wall track system (WTS, ESOATE, Maastricht, The Netherlands) and the ART.LAB (ESAOTE, Maastricht, The Netherlands) in immediate succession.

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## Diastolic dysfunction (DD)

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Assessment of left ventricular DD was performed according to the recommendations of the American Society of Echocardiography and the Committee of the European Association of Echocardiography [35, 36], using the following grading scheme: mild or grade I (impaired relaxation pattern), moderate or grade II (pseudonormal filling) and, severe (restrictive filling) or grade III. Due to the relatively small number of subjects (n=3) with grade III DD in our study population, it was decided to consider the variable as no DD, grade I and grade II/III.

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## Left ventricular mass

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Echocardiographic examinations of the subject in the left lateral decubitus position were performed by an experienced echocardiographer using a commercially available standard ultrasound scanner (Vivid E9, General Electric Medical Systems, Horten, Norway) with a 2.5-MHz phased-array transducer (M5S). The echo/Doppler examinations included exhaustive examinations in parasternal long- and short-axis views and in the standard apical views [35]. All acquired images and media were stored on a secured network server as digital videos with
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unique identification numbers and analyzed on a dedicated workstation (EchoPAC PC, version 110.1.0, GE Healthcare). Notably, the left ventricular internal diastolic diameter from the parasternal 2-dimensional long-axis view was measured. These measurements were subsequently used in the cube-function formula of the American Society of Echocardiography guidelines to calculate left ventricular mass (LVM) [37], which was then indexed for height to the 2.7 power [38].

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## Covariates

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During the fourth visit, all participants underwent a clinical examination measuring weight (kg), height (m), and waist circumference (cm). Body mass index was calculated as weight (kg) divided by the square of height (m). Blood samples were also collected, and serum concentrations of the following biomarkers were measured: fasting blood glucose, HDL cholesterol, and triglycerides. LDL cholesterol was calculated using the Friedewald formula: $[LDL-cholesterol] = [total cholesterol] - [HDL-cholesterol] - [triglycerides]/5$ [39, 40].

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Office blood pressure (BP) was also measured. After 10 minutes of rest in the supine position, systolic and diastolic blood pressures were measured with an automatic device (Dinamap Pro 400, CRITIKON). Office BP was measured 3 times at 1-minute intervals, and the mean of the 3 measurements was calculated. Blood samples were collected during the clinical examination.

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Additionally, participants completed several questionnaires assessing their socio-demographic characteristics such as education level, medical history and treatment, as well as their level of physical activity using the International Physical Activity Questionnaire (IPAQ).

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254 Statistical analyses

255 Participant characteristics were first assessed globally. The cohort was stratified into quartiles
256 based on total lipid intakes from milk-related sources (g/day), among which SFAs represent
257 62.3%. Within each quartile, medians and interquartile ranges were calculated for each variable.

258 Differences between quartiles were assessed using the Kruskal-Wallis test for quantitative
259 variables and chi-square test or Fisher's exact test for qualitative variables, applying the
260 Benjamini-Hochberg correction for multiple testing.

261 Second, dairy lipid intakes were examined from six types of milk-related products: Milk,
262 Yogurt, Ripened Cheese, Unripened Cheese, Cream, Butter (including dairy spreads), as well
263 as Dairy desserts (Supplementary Table 1). The term ‘dairy lipids’ was used at this first stage
264 since easier to visualize for a wide audience (62.3% of dairy lipids being composed of SFAs).

265 Dietary intakes and the distribution of milk-related lipid intake were calculated by quartile of
266 dairy lipid consumption, with an additional stratification by generation: Generation 1 (parents
267 - aged over 50) and Generation 2 (offspring aged over 18 and under 50).

268 Third, the relationships between dairy SFA intake and cardio-metabolic outcomes were
269 assessed, analyzing the dietary intake and matrix effect at different levels: 1) total dietary lipids,
270 2) total dietary SFAs, 3) SFAs specifically from dairy products or from dairy fat sources, 4)
271 SFAs from six types of milk-related products (Milk, Yogurt, Ripened Cheese, Unripened
272 Cheese, Butter (including dairy spreads), Cream and Dairy desserts). The latter were analyzed
273 in association with seven different outcomes, divided into four categories: organ damages (i.e.
274 cfPWV, cIMT, DD and LVM), combined pre-diabetes and diabetes, MetS and hyperlipidemia.

275 Models were resolved using the lme4 package (v1.1.29) [41], with confidence intervals from
276 broom.mixed (v0.2.9.4) and p-values from lmerTest (v3.1.3) [41].
Three levels of adjustment were applied for each model, progressively incorporating additional covariates: 1) adjusted for age and sex, 2) additionally adjusted for waist circumference, tobacco use, education level, DASH score, number of ultra-processed foods servings, total energy intake and alcohol intake, 3) additionally adjusted for the use of antihypertensive, antidiabetic, and lipid-lowering drugs, as well as adherence to a diet which allowed assessing changes in the usual diet, which is key information in a cross-sectional study.

For the diabetes/prediabetes outcome, the 'hypoglycemic treatment' variable was excluded from the predictors since individuals under treatment are systematically diabetic. Similarly, 'waist circumference' was omitted in models for MetS, as they are part of its criteria, as well as the variable "lipid-lowering drugs" for hyperlipidemia outcomes. In the context of a family cohort, mixed-effects models with a random family effect were used. Linear mixed-effects models (LMMs) were applied to quantitative outcomes, while generalized linear mixed-effects models (GLMMs) were used for qualitative outcomes. For LMMs, outcomes were log-transformed to address heteroskedasticity frequently observed in the data.

Sensitivity analyses

Due to 291 missing values for physical activity, this variable was excluded from the main analyses to avoid loss of statistical power. Sensitivity analyses were conducted to assess its impact when included in the models. Two steps were followed: 1) *Dataset restriction*: Models were adjusted using data from individuals with physical activity information but without including this variable, assessing the loss of power due to the restricted sample size. 2) *Variable inclusion*: Physical activity was added to the models, using the same restricted dataset, to assess its influence on model coefficients.
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Additionally, individuals were compared with and without missing physical activity data.

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Quantitative variables were analyzed using the Wilcoxon test, while qualitative variables were

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assessed using the chi-square test (for expected cell counts ≥5) or Fisher's exact test (for smaller counts).

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All statistical analyses were performed using the R software (version 4.1.3).

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## Results

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### Characteristics of the study population

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In the STANISLAS cohort, the median [interquartile range] consumption of lipids from all milk-related products (dairy products and dairy fat sources) was 23 [15, 34] g/day. The participants in the fourth quartile were more likely to be men, have a higher physical activity level, and less likely to be dieting or take lipid-lowering drugs (Table 1, and Supplementary Table 2). An increase in caloric and macronutrient intake from Q1 to Q4 was also observed (Table 2). Moreover, the contribution of dairy lipids to total lipid intake, assessed by the dairy lipids/total lipids ratio, also increased from 0.16 in Q1 to 0.36 in Q4. The participants in Q4 also had a higher intake of ultra-processed foods, and a lower DASH score, suggesting a lower overall nutritional quality (Supplementary Table 2). The prevalence of each binary outcome in the whole cohort was as follows: 40% (N=637) diabetes/prediabetes, 25% (N=402) MetS, and 18% (N=273) diastolic dysfunction. Regarding hyperlipidemia, the prevalence was 27% (N=440) for hypertriglyceridemia, 35% (N=571) for elevated LDLc, 32% (N=510) for elevated non-HDL-cholesterol, and 22% (N=362) for elevated ApoB.

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## Contribution of the various dairy product types to dairy lipid intake

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Although 99.8% of participants consumed dairy products, the types of dairy products varied significantly. Cheese was the most consumed product (97.5% of consumers), while Milk was the least consumed (54.8% of consumers) (Supplementary Table 3). Most milk-related products contributed uniformly to increased milk-related lipid intake (Figure 2), except Butter, which increased 4.5-fold from the first to the last quartile (3.8% to 17.2% of total milk-related lipids, respectively). Ripened cheese and Butter, the main sources of dairy lipids, showed generational differences—generation 1 consumed less Butter and more Ripened cheese, except among the highest dairy lipid consumers, where the generational difference in Butter intake disappeared (Figure 3). Lipids from milk-related products represented 16 to 36% of total dietary lipid intake from the first to the last quartile (Supplementary Table 3). The contribution of milk-related SFA intake in the total dietary SFA intake ranged increasingly from 43% to 80% from Q1 to Q4, i.e. milk-related SFA represent the major SFA source for subjects in the second, third and fourth quartiles (Supplementary Table 3).

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## Association between lipids and cardio-metabolic outcomes

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In these analyses, we investigated the association with cardio-metabolic outcomes in a step-by-step manner by refining the level of detail relative to the lipids. No significant associations were globally observed between total dietary lipids, total dietary SFA and SFA from dairy products and hyperlipidemia (Figure 4), although in the fully adjusted models, total dietary SFA was significantly and negatively associated with elevated ApoB (Figure 4D - OR [95% CI]: 0.970 (0.953-0.988)). SFA from dairy fat sources was significantly and negatively associated with the 4 hyperlipidemia variables (Figures 4A, B, C, D): 0.962 (0.931-0.994) for hypertriglyceridemia, 0.962(0.933-0.991) for elevated LDLc, 0.950 (0.921-0.979) for elevated non-HDLc, 0.926 (0.892-0.961) for elevated ApoB. This association was driven by Butter, with SFA intake
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showing a similar inverse relationship with all hyperlipidemia variables: OR [95% CI]: 0.962
[0.931–0.994] for hypertriglyceridemia (Figure 5, 0.962 (0.933-0.991) for elevated LDLc
(Figure 6), 0.950 (0.921-0.979) for elevated non-HDLc (Figure 7), 0.926 (0.892-0.961) for
elevated ApoB (Figure 8).

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In the sensitivity analyses, the association between SFA intake from Butter and non-HDLc and ApoB remained significant in the restricted cohort (by removing the participants with no data on physical activity without adjusting for the latter), and also by adjusting on physical activity in the restricted cohort (Figures 7 and 8). However, although SFAs from Butter were associated with hypertriglyceridemia in a model adjusted for age and sex (0.960 [0.931–0.991]), the significance was lost after further adjustment (0.972 [0.937–1.008]) (Figure 5B). Regardless of the adjustment or not for physical activity, the association with hypertriglyceridemia was no longer observed in the restricted cohort (Figure 5C). Regarding hypercholesteremia, the association with SFA from Butter became marginally significant in the restricted cohort in fully adjusted models (p=0.051, Figure 6). Of note, participants with available physical activity data and those without physical activity data, defining the differences between the whole and restricted cohorts, had altogether similar characteristics (Supplementary Table 4). Therefore, we cannot rule out that a lack of power may explain the loss of significant associations between Butter SFA intake and hypertriglyceridemia and elevated LDLc in the restricted cohort with the available physical activity data. Additionally, participants with hyperlipidemia had lower intake of Butter than participants without hyperlipidemia (Supplementary Table 5).

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SFA from Yogurt showed no association with hypertriglyceridemia, elevated LDLc and elevated non-HDLc. However, SFA from Yogurt was significantly and negatively associated with elevated ApoB in the whole population (0.921 [0.864-0.981] - Figure 8A), and in the restricted cohort, both with (0.913 [0.846-0.984] - Figure 8C) or without adjustment on physical activity (0.912 [0.846-0.983] - Figure 8B).
For left ventricular mass, the main analysis showed inverse associations with total dietary SFAs ($\exp(\beta)$ [95% CI]: 0.849 [0.739–0.976]) and SFAs from dairy products (0.997 [0.996–0.999]), although these associations disappeared in sensitivity analyses (Table 3). SFAs from Yogurt were inversely associated with left ventricular mass, albeit marginally significant after adjusting for medications and dieting.

Pulse wave velocity was associated with total lipids in the fully adjusted models (0.961 [0.924–0.999]), with this association remaining in the sensitivity analysis (Supplementary Table 6). None of the SFA-related types of dairy products was associated with pulse wave velocity (Supplementary Table 6).

MetS was positively and significantly associated with SFAs from Unripened cheese in the main analysis, although this association lost significance after adjustment for medications and dieting (Figure 9). None of the other lipid sources was associated with MetS (Figure 9).

Lastly, no association was observed between any of the milk-related lipids or SFAs and any of the other outcomes, i.e. diabetes and prediabetes, DD and intima media-thickness, whether in the main (Figures 10, and 11, Supplementary Table 7) or sensitivity analyses (data not shown).

## Discussion

In the STANISLAS cohort, higher total dietary lipid intake was associated with lower arterial stiffness, while higher total dietary SFAs and SFAs from dairy products were favorably associated with lower left ventricular mass although all these latter associations were no longer significant in the sensitivity analysis. Higher SFAs from dairy fat sources, particularly Butter, were associated with a lower occurrence of hyperlipidemia, namely hypertriglyceridemia, elevated LDLc, elevated non-HDLc, elevated ApoB. The associations of SFA from Butter with elevated non-HDLc and with elevated ApoB remained in the sensitivity analyses. However,
this was not the case for hypertriglyceridemia and elevated LDLc where the associations were
not significant anymore. SFA from Yogurt was associated with a lower occurrence of elevated
ApoB in the main and sensitivity analyses. Total dietary SFAs or from any of the milk-related
products were not associated with any other cardio-metabolic outcomes.

Certain recent meta-analyses did not indicate any association between total dietary SFA intake
and CVD [12], while others suggested different effects according to food matrix [13]. A food-
based study approach in the Multi-Ethnic Study of Atherosclerosis cohort reported lower CVD
risk with higher SFA intake from dairy products (incl. yogurts, ice creams, milks pure or in a
beverage, cottage cheeses, so-called “regular” cheeses – i.e. Cheddar, American, Swiss…,
cream sauce/soup, pizza, enchiladas without meat; excl. butter) but higher risk with SFA intake
from meat (unprocessed and processed meats, pure on in meat-containing foods as hamburgers,
burritos etc) [13]. Authors highlight the role of food-specific fatty acids and nutrients, in
addition to SFAs, as well as other structural characteristics of the food [13]. The Framingham
offspring study reports that adult men with higher intakes of “dairy SFAs” had a less
atherogenic profile than males with lower intakes of these fats [42]: in this study, SFA derived
from all types of dairy products and dairy fat sources were considered (milk, yogurt, cheese,
butter, cream, either as individual foods or as ingredients in other food products).

In this setting, our current study focusing mainly on SFAs from specific subtypes of milk-
related products providing dairy fat showed an inverse association between SFA intake from
Dairy products and left ventricular mass in the main analyses, although not significant in the
sensitivity analyses likely due to reduced power. While left ventricular hypertrophy is
understudied, our findings align with evidence that frequent dairy consumption in children
reduces the risk of left ventricular hypertrophy [43].

When considering CVD more globally, recent meta-analyses of prospective studies report null
or weak inverse associations between total dairy consumption and CVD risk and mortality [44–
420 47]. A recent review of dose-response results of large prospective cohort studies and meta-analyses examining the association of dairy food consumption and risk of cardiovascular outcomes concludes notably that: (i) total dairy intake is either not or favorably associated with CVD risk; (ii) there is no conclusive evidence that regular-fat and low-fat dairy foods is differentially associated with any CVD-related clinical outcomes; and (iii) consumption of fermented dairy, primarily yogurt and cheese, in general shows no association with CVD risk [24]. Focusing on subclinical CV damages, our study reveals that only left ventricular mass was inversely associated with SFA intake from Dairy products, and not atherosclerosis or arterial stiffness. As increased left ventricular mass is a known risk factor for CVD [48], this result suggests a potential mechanism linking SFA from dairy products to CVD via left ventricular mass modulation. When examining associations at the product level, our findings show that SFAs from Yogurt were significantly inversely associated with left ventricular mass. Although this association was no longer significant in the fully adjusted model, including medication and dieting, it nonetheless appears consistent with reviews that have concluded to a neutral to beneficial effect of fermented dairy on CVD [24, 49], and also more recently with the French Nutrinet Santé cohort study (no significant associations between total dairy, milk, cheese, yogurts, fermented and reduced-fat dairy intakes and CVD risk; but an observed reduced risk of cerebrovascular diseases, including stroke, with the consumption of fermented dairy, including cheese and yogurts) [50]. Furthermore, SFAs from Yogurt were also significantly and inversely associated with elevated ApoB (an increasingly used marker of CVD risk) in both the main and sensitivity analyses, albeit not associated with other markers of hyperlipidemia. This finding may be explained by the fact that ApoB is not necessarily correlated with LDLc and non-HDLc, as ApoB more accurately reflects the concentration of atherogenic lipoprotein particles (i.e. LDL, VLDL, chylomicrons and their remnants) rather than the cholesterol within [51–53]. This result raises the question of a potential causal effect of SFA within yogurts on the
amount of such lipoproteins. Supporting our finding, a 4-week randomized crossover trial comparing the effects of 1.5-2 portions/day of full-fat yogurt (4% fat), versus low-fat yogurt and butter (providing an equivalent fat amount than the full-fat yogurt), on cardiometabolic risk factors found that ApoB levels were significantly decreased following the full-fat yogurt intervention compared with the low-fat yogurt plus butter combination [54], while no differential effect was observed for TG, LDLc and non-HDLc in this trial. Altogether, as fermentation is reported to impact food lipid composition [55], this raises the question as of whether milk lipid fermentation by yogurt bacteria may ultimately contribute to a specific beneficial impact on ApoB levels. More generally, the fermentation of dairy products is known to produce bioactive peptides that are initially embedded in milk proteins and released by the proteolytic activity of lactic acid bacteria [56]. These peptides exhibit antihypertensive properties, likely due to their ability to inhibit the angiotensin-converting enzyme [57]. Additionally, yogurt naturally contains lactic acid bacteria with probiotic properties, promoting favorable changes in gut microbiota such as increased beneficial *Lactobacillus* and *Bifidobacterium* genera, which may help to prevent CVD [58]. Whether such mechanisms could contribute to the observed favorable associations of SFA from Yogurt with left ventricular mass and ApoB will deserve further investigation.

No associations were found between SFA intake (total or from milk-related sources) and pre-diabetes and diabetes in the present study. Nonetheless, there is conflicting evidence in the literature with one meta-analysis having found significant inverse associations between intakes of dairy products, low-fat dairy products, and cheese, and the risk of type 2 diabetes [59], whereas when focusing on total dietary SFAs, others did not find any significant association with the risk of type 2 diabetes [60]. According to Soedamah-Muthu et al. [17], the results from all studied meta-analyses consistently showed neutral or inverse associations for total and low-
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fat dairy products, with the most striking inverse association observed for yogurt and type 2 diabetes. A more recent narrative review confirmed that consumption of yogurt appears to be associated with a lower risk of developing type 2 diabetes [49]. This was supported by the FDA in March 2024 which concluded that there is some credible evidence supporting a relationship between yogurt intake and reduced risk of type 2 diabetes, irrespective of fat content [61]. In the present study, we did not find any association between SFAs from Yogurt and type 2 diabetes including pre-diabetes, supporting the fact that emphasis should not be placed on the SFA content of the yogurt matrix.

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Regarding hyperlipidemia, saturated fats are generally reported to raise blood LDL cholesterol and ApoB levels, key etiological CVD risk factors [4]. The Reading, Imperial, Surrey, Saturated fat Cholesterol Intervention ("RISSCI")-1 study showed that transitioning from higher-SFA/lower-Unsaturated FA (UFA) to a lower-SFA/higher-UFA diet significantly reduced fasting blood lipids such as LDLc, ApoB and non-HDLc [62]. In a dietary intervention RCT in men and women aged 50-75 years, consuming 50 g of butter per day during 4 weeks increased LDLc compared to 50 g/day of olive oil [63]. Here in a study population aged between 18 and 88 years and consuming lower butter amounts (maximum 10 [4 - 20] g/day in the higher quartile of milk-related lipid intake), we found that SFA from Butter was negatively associated with hypertriglyceridemia, elevated LDLc but also high non-HDLc and high ApoB. Importantly, among LDL particles, small- and medium-density LDL are considered more atherogenic due to decreased hepatic clearance by the LDL receptor and enhanced anchoring to LDL receptor-independent binding sites in extrahepatic tissues, including the arterial wall [64]. In this respect, a cross-over RCT [65] demonstrated that the observed increase in serum cholesterol from high-saturated fat diets stems from an increase in the concentration of large LDL particles, whereas small- and medium-sized LDL particles remain unaffected. Regarding

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Butter specifically, a RCT in obese individuals comparing diets rich in SFAs from cheese or butter, or rich in MUFA or PUFA, or a low-fat, high-carbohydrate diet, suggests that SFAs from butter and cheese have similar effects on features of the LDL particle size phenotype, and that consumption of SFAs from butter may be associated with larger LDL particles, i.e., less atherogenic [66]. With regard to CV events, an epidemiological study reported that a higher intake of butter (>1 serving per day) versus zero intake was not associated with a higher risk of the composite of mortality or major cardiovascular events (defined as death from cardiovascular causes, non-fatal myocardial infarction, stroke, or heart failure) [67]. Furthermore, several meta-analyses showed that butter consumption was not significantly associated with CVD, coronary heart disease and stroke [45, 47]. Altogether, the observation herein that Butter SFA was negatively associated with hyperlipidemia is consistent with the above latter. These favorable associations with Butter may be partly explained by (i) the butter consumption amount in the present cohort, which is close to the French recommendations i.e. not in excess, and (ii) the composition of this important milk fat source, containing several bioactive fatty acids (e.g. short chain, natural trans, branched chain, conjugated fatty acids). However, as these FA are also naturally present in dairy products but no association is observed here between other dairy SFA sources and hyperlipidemia, we cannot exclude a reverse causation in the observed cross-sectional inverse association between Butter SFA intake and hyperlipidemia. Participants with hyperlipidemia had lower intake of SFA from Butter, compared to participants without hyperlipidemia: this can be the reflect of different long-term dietary habits but we cannot rule out that this could be the result of recent or older medical or dietary advices, even if our analyses adjusted on current dieting information to limit such a potential confounding behavior.

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Altogether, increasing evidence supports that other components of milk lipid sources and of dairy products are more relevant than SFA content alone on cardio-metabolic health. As mentioned above, dairy products and dairy fat sources contain numerous other types of fatty acids with reported favorable biological activities (e.g. trans-vaccenic acid, trans-palmitoleic acid, conjugated linoleic acid) [1, 24]. In dairy products, other components may interfere with the intestinal absorption and digestibility of fat (e.g., calcium, notably in hard cheese, can reduce SFA absorption) and, therefore, attenuate the effect of SFAs on cholesterol metabolism and potentially provide a protective effect on cardiovascular health [68]. Among lipid molecules carrying SFAs in dairy products, milk polar lipids have been shown to reduce LDLc, ApoB and triglyceride concentrations as well as reduce intestinal cholesterol absorption in at-risk individuals in randomized dietary intervention trials [20, 21]. Suggested mechanisms are related to the impact of milk sphingomyelin that can reduce cholesterol absorption and improve circulating sphingolipid profile [69]. In a Chinese cohort study of ~2000 participants (dairy consumption being assessed as intake frequency and amount of milk, milk formula, yogurt, ice cream, milk tablet, cream, evaporated milk; i.e. excluding butter) (replicated in an intervention trial) [70] and in the large Spanish PREDIMED cohort (validated in confirmatory US cohorts, namely NHS, NHSII and HPFS) ("total dairy" being considered here as milk, yogurt, cheese, cream, ice cream and butter) [71], the circulating concentration of milk-specific sphingomyelin species (C14-SM) (i) was positively associated with total dairy intake, (ii) showed favorable associations with changes or incidences of blood pressure and fasting glucose in the Chinese participants with habitually low dairy consumption [70], and (iii) contributed to a dairy-intake score, a higher score being associated with a lower T2D risk [71]. Altogether, this supports a potential causal biological pathway between dairy intake (either including or excluding butter according to the studies), the identified circulating lipidomic profile, and the prevention of cardio-metabolic diseases [70, 72]. Finally, postprandial lipemia is another important
544 independent risk factor for CVD: recent research shows that the structure and texture of the
545 different SFA-containing dairy matrices (e.g. emulsified vs spread milkfat; hard cheese vs
546 cream cheese) can modulate lipid digestion and postprandial lipemia [23, 73]. Hence, the health
547 effect of the food as a whole rather than the content of any single nutrient may therefore be
548 more relevant to understanding the associations between dietary consumption and health
549 outcomes. Although the present work does not show major differences in the associations of
550 SFA with cardiometabolic outcomes between the different matrices studied (Milk vs Yogurt vs
551 Cheese vs Butter vs Cream notably), they align with recent reports that there is no deleterious
552 association between dairy intake and CVD risk despite their SFA content [24].

553

554 The present study features several strengths. The analyses were based on a population-based
555 cohort with the availability of detailed information on diet, in particular detailed information
556 on milk-related products, lifestyle and clinical factors and outcomes, as well as extensive
557 cardiovascular phenotyping. The present is also one of the first studies to focus on detailed
558 milk-related products in relation to cardio-metabolic health. However, certain limitations of this
559 study should be acknowledged. First, the results are based on cross-sectional data, and causality
560 cannot be implied due to the observational nature of the study. Second, physical activity data
561 were not available for all participants and could therefore only be partially taken into account,
562 despite its known importance in lifestyle-related diseases. Nonetheless, in a sensitivity analysis
563 limited to individuals with available physical activity data, adjusting for physical activity did
564 not alter the major results. Third, food intake may have changed over the life course because of
565 age or disease diagnosis. However, our cross-sectional study design precluded us from
566 recording these changes, which may influence the observed association, although dieting was
567 accounted for as a confounder to minimize this effect. Longitudinal studies are needed to better
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understand the association between SFA intake from different dairy/milk-related matrix sources
and cardio-metabolic outcomes. Lastly, the generalizability of our results may be limited due
to the inclusion of participants solely from the Lorraine region, albeit with a similar intake of
total dairy to that of the rest of France, but with less milk intake and greater cheese intake
compared to Nutrinet Santé [50] and INCA3 [74] reports. As dairy fat sources and dairy
products provided up to 80% of total dietary SFA in the present cohorts, observed associations
between total dietary SFA and cardiometabolic outcomes may have been driven by the milk-
related sources.

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In conclusion, the present results align with recent literature indicating that SFAs from dairy
products present neutral to beneficial associations with cardio-metabolic health. They
contribute to the “dairy matrix” concept in which the specific compositions and structures of
dairy products and dairy fat sources more likely explain their relationship with cardiovascular
health or some blood lipid markers of CVD risk than the SFAs within. Our study also delves
deeper into the specific subtypes of milk-related products, adding new insight to current
knowledge exploring the dairy matrix effect. Further studies are needed to better understand
these relationships in larger longitudinal cohorts and *via* RCTs in different populations, as well
as exploring the health impact of other nutrients and micronutrients of the different dairy
matrices, such as calcium, along with fermentation.

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592
593 **Declarations**

594

595 **Ethics approval and consent to participate**

596 The research protocols were all approved by the local Ethics Committee (Comité de Protection

597 des Personnes Est III— Nancy—France), and all study participants gave informed written

598 consent. The informed written consent was previously approved by the local Ethics Committee.

599

600 **Consent for publication**

601 Not applicable

602

603 **Availability of data and materials**

604 The datasets used and/or analyzed during the current study are available from the corresponding

605 author upon reasonable request.

606

607 **Statements and Declarations**

608 **Competing interests**

609 M.-C. M. received research funding from CNIEL, Sodiaal-Candia R&I, and Danone Nutricia

610 Research, congress travel funding from CNIEL and symposium honorarium from IMGC, which

611 are not related to the present epidemiological study.

612 PR received grants and personal fees from Vifor Fresenius Medical Care Renal Pharma,

613 personal fees from Idorsia, personal fees from KBP, Sanofi, NovoNordisk, personal fees from

614 Ablative Solutions, non-financial support from G3P, personal fees from Corvidia, grants,

615 personal fees from Relypsa, a Vifor company, and Vifor, personal fees from CardioRenal,

616 grants and personal fees from AstraZeneca, grants and personal fees from Bayer, grants and

617 personal fees from CVRx, personal fees from Fresenius, grants and personal fees from Novartis,
618

personal fees from Grunenthal, personal fees from Servier, personal fees from Stealth Peptides,
all outside the submitted work.

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NG received honoraria from AstraZeneca, Bayer, Boehringer, Echosens, Lilly, Roche
diagnostics, Novartis. PR: reports consulting for Idorsia, G3P, honoraria from AstraZeneca,
Bayer, Boehringer-Ingelheim, Cincor, CVRx, Fresenius, KBP biosciences, Novartis,
NovoNordisk, Relypsa, Servier, and Vifor Fresenius Medical Care Renal Pharma; and travel
grants from AstraZeneca, Bayer, CVRx, Novartis, and Vifor Fresenius Medical Care Renal
Pharma; Cofounder: CardioRenal, all outside the submitted work.

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The other co-authors have no conflicts of interest to disclose related to the submitted work, and
declare that the research was conducted in the absence of any commercial or financial
relationships that could be construed as a potential conflict of interest.

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Source of funding

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This work was funded by the FRM (Fondation pour la Recherche Médicale, DAILICATE project, Equipe FRM EQU202103012668, coordinated by M.C. Michalski).

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The fourth examination of the STANISLAS study was sponsored by the Centre Hospitalier
Régional Universitaire of Nancy (CHRU) and the French Ministry of Health (Programme
Hospitalier de Recherche Clinique Inter-régional 2013), by the Contrat de Plan Etat- Lorraine
and “Fonds Européen de Développement Régional” (FEDER Lorraine), and by a public grant
overseen by the French National Research Agency (ANR) as part of the second
“Investissements d'Avenir” program FIGHT-HF (reference: ANR-15-RHU-0004) and by the
French “Projet investissement d'avenir” (PIA) project “Lorraine Université d'Excellence”
(reference ANR-15-IDEX-04-LUE). The STANISLAS study is also supported by the sixth
European Union—Framework Program (EU-FP) Network of Excellence Ingenious HyperCare
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(#LSHM-CT-2006-037093), the seventh EU-FP MEDIA (Européen Cooperation—Theme

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Health/FP7HEALTH-2010-single-stage #261409), HOMAGE (Heart Omics in Aging, 7th

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Framework Program grant #305507), FOCUS-MR (reference: ANR-15-CE14-0032-01), and

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FIBROTARGETS (FP7#602904) projects, and by ERA-CVD EXPERT (reference: ANR-16-

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ECVD-0002-02).

647

Author contributions

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All authors have read and agreed to the published version of the manuscript. PR, NG, and JMB

649

designed the fourth visit of the STANISLAS cohort. EB and NG supervised the cardiovascular

650

(arterial stiffness and thickness, and echocardiography, respectively) assessments. LM

651

performed the data management. SW, MCM, and NG designed the present research. TM, CL,

652

KY performed the statistical analysis. SW and MCM drafted the first version of the manuscript.

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All authors were involved in the interpretation of the results and the critical review of the

654

manuscript.

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656

Acknowledgments

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We thank Emmanuelle Meugnier (CarMeN laboratory) for providing statistical advice, Pierre-

658

Luc Maclot for his support and technical assistance, and are grateful to the Department of Public

659

Health of the University of Liège (Belgium) for allowing us to use their food frequency

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questionnaire (https://www.dssp-uliege.be/FFQ) during the fourth visit. We thank P. Pothier

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for English proofreading.

662

The authors deeply thank the staff of the Clinical Investigation Center and other personnel

663

involved in the management of the STANISLAS Cohort: Biostatisticians: Fay R, Lamiral Z,

664

and Machu JL. Computer scientists: Boucenna N, Gallina-Müller C, Maclot PL, and Sas T. Co-
665 investigators: Chau K, Di Patrizio P, Dobre D, Gonthier D, Huttin O, Malingrey L, Mauffrey
666 V, Olivier A, Poyeton T, Steyer E, and Watfa G. Data managers: Cimon P, Eby E, and LM.
667 Data entry operators: Batsh M, Blanger O, Bottelin C, Haskour N, Jacquet V, Przybylski MC,
668 Saribekyan Y, Thomas H, and Vallée M. Echocardiographers and ultrasonographers: Ben Sassi
669 M, Cario S, Camara Y, Coiro S, Frikha Z, Kearney-Schwartz A, Selton-Suty C, and Watfa G.
670 Imaging engineer: EB. Laboratory Engineer: Nuée-Capiaumont J and Technicians: Fruminet J,
671 Kuntz M, Ravey J, Rousseau E, and Tachet C. Project managers: Bouali S and Hertz C. Quality
672 engineer: Lepage X. Registered Nurses: Giansily M, Poinsignon L, Robin N, Schmartz M, Senn
673 M, Micor-Patrignani E, and Toutlemonde M. Hospital technician: Fleurot MT. Resident
674 physicians: Alvarez-Vasquez R, Amiot M, Angotti M, Babel E, Balland M, Bannay A, Basselin
675 P, Benoit P, Bercand J, Bouazzi M, Boubel E, Boucherab-Brik N, Boyer F, Champagne C,
676 Chenna SA, Clochey J, Czolnowski D, Dal-Pozzolo J, Desse L, Donetti B, Dugelay G, Friang
677 C, Galante M, Garel M, Gellenoncourt A, Guillin A, Hariton ML, Hinsiger M, Haudiquet E,
678 Hubert JM, Hurtaud A, Jabbour J, Jeckel S, Kecha A, Kelche G, Kieffert C, Laurière E, Legay
679 M, Mansuy A, Millet- Muresan O, Meyer N, Mourton E, Naudé AL, Pikus AC, Poucher M,
680 Prot M, Quartino A, Saintot M, Schiavi A, Schumman R, Serot M, Sert C, Siboescu R, Terrier-
681 de-la-Chaise S, Thiesse A, Thietry L, Vanesson M, and Viellard M. Secretaries: De Amorin E,
682 Villemain C, and Ziegler N. Study Coordinators: Dauchy E, Laurent S, and all persons not listed
683 above who helped in the funding, initiation, accrual, management, and analysis of the fourth
684 visit of the STANISLAS cohort. The authors thank the Biological Resource Center Lorrain BB-
685 0033-00035 of Nancy CHRU for biobank handling. Steering committee: Pierre Mutzenhardt,
686 Mehdi Siaghy, Patrick Lacolley, Marie-Ange Luc, Pierre-Yves Marie, and Jean-Michel
687 Vignaud. Advisory members: Sophie Visvikis Siest and Faiez Zannad. Technical committee:
688 Christiane Branlant, Isabelle Behm Ansmant, Jean-Michel Vignaud, Christophe Philippe,
689 Jacques Magdalou, Faiez Zannad, and PR. Scientific committee: Laurence Tiret, Denis Wahl,
690 Athanase Benetos, Javier Diez, Maurizio Ferrari, Jean Louis Gueant, Georges Dedoussis, 691 François Alla, François Gueyffier, Pierre-Yves Scarabin, Claire Bonithon Kopp, Xavier 692 Jouven, Jean-Claude Voegel, and Jan Staessen. They also thank all of the Stanislas cohort 693 participants.

694

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<table><caption>Table 1. Participant characteristics according to quartiles of lipid intake from milk-related products</caption><thead><tr><th rowspan="3">Characteristics</th><th colspan="5">Quartile of lipid intake from milk-related products<sup>2</sup></th><th rowspan="3">p-value<sup>1</sup></th></tr><tr><th>Overall</th><th>Q1</th><th>Q2</th><th>Q3</th><th>Q4</th></tr><tr><th>[0-120] (g/d)</th><th>[0-15] (g/d)</th><th>[15-23] (g/d)</th><th>[23-34] (g/d)</th><th>[34-120] (g/d)</th></tr></thead><tbody><tr><td>N</td><td>1632</td><td>408</td><td>408</td><td>408</td><td>408</td><td></td></tr><tr><td>Age</td><td>56 (34, 60)</td><td>56 (35, 61)</td><td>56 (35, 60)</td><td>55 (35, 60)</td><td>55 (34, 60)</td><td>0.555</td></tr><tr><td>Male</td><td>794 (49%)</td><td>164 (40%)</td><td>200 (49%)</td><td>201 (49%)</td><td>229 (56%)</td><td>&lt;0.001</td></tr><tr><td>Blood glucose (g/L)</td><td>0.88 (0.82, 0.95)</td><td>0.88 (0.82, 0.94)</td><td>0.88 (0.83, 0.95)</td><td>0.88 (0.83, 0.95)</td><td>0.89 (0.82, 0.96)</td><td>0.861</td></tr><tr><td>Blood cholesterol (g/L)</td><td>2.10 (1.85, 2.41)</td><td>2.12 (1.83, 2.43)</td><td>2.10 (1.87, 2.41)</td><td>2.11 (1.86, 2.36)</td><td>2.08 (1.85, 2.39)</td><td>0.840</td></tr><tr><td>Triglycerides (g/L)</td><td>0.92 (0.67, 1.26)</td><td>0.96 (0.68, 1.26)</td><td>0.90 (0.67, 1.23)</td><td>0.93 (0.70, 1.31)</td><td>0.88 (0.65, 1.24)</td><td>0.448</td></tr><tr><td>HDL cholesterol (g/L)</td><td>0.57 (0.48, 0.66)</td><td>0.57 (0.48, 0.66)</td><td>0.56 (0.48, 0.66)</td><td>0.57 (0.48, 0.67)</td><td>0.55 (0.48, 0.66)</td><td>0.674</td></tr><tr><td>LDL cholesterol (g/L)</td><td>1.31 (1.11, 1.56)</td><td>1.32 (1.08, 1.58)</td><td>1.32 (1.13, 1.56)</td><td>1.30 (1.10, 1.53)</td><td>1.32 (1.10, 1.55)</td><td>0.724</td></tr><tr><td>Apolipoprotein A1 (g/L)</td><td>1.67 (1.49, 1.88)</td><td>1.69 (1.51, 1.90)</td><td>1.65 (1.49, 1.87)</td><td>1.67 (1.48, 1.88)</td><td>1.67 (1.50, 1.88)</td><td>0.674</td></tr><tr><td>Apolipoprotein B (g/L)</td><td>0.96 (0.81, 1.12)</td><td>0.97 (0.81, 1.15)</td><td>0.97 (0.84, 1.12)</td><td>0.96 (0.81, 1.10)</td><td>0.96 (0.80, 1.12)</td><td>0.747</td></tr><tr><td>Office SBP</td><td>123 (114, 135)</td><td>123 (113, 135)</td><td>123 (114, 135)</td><td>123 (115, 135)</td><td>124 (115, 135)</td><td>0.747</td></tr><tr><td>Antihypertensive treatment</td><td>333 (20%)</td><td>92 (23%)</td><td>79 (19%)</td><td>85 (21%)</td><td>77 (19%)</td><td>0.674</td></tr><tr><td>Lipid-lowering treatment</td><td>249 (15%)</td><td>79 (19%)</td><td>60 (15%)</td><td>62 (15%)</td><td>48 (12%)</td><td>0.040</td></tr></tbody></table>
<table><tr><td>Glucose-lowering treatment</td><td>60 (3.7%)</td><td>19 (4.7%)</td><td>16 (3.9%)</td><td>9 (2.2%)</td><td>16 (3.9%)</td><td>0.404</td></tr><tr><td>Metabolic syndrome</td><td>407 (25.1%)</td><td>116 (28.4%)</td><td>92 (22.8%)</td><td>93 (23.0%)</td><td>106 (26.0%)</td><td>0.191</td></tr><tr><td>Diabetes or prediabetes</td><td>640 (39.8%)</td><td>167 (41.4%)</td><td>156 (38.6%)</td><td>164 (40.6%)</td><td>153 (38.6%)</td><td>0.797</td></tr><tr><td>Hyperlipidemia</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Hypertriglyceridemia</td><td>442 (27.2%)</td><td>131 (32.3%)</td><td>104 (25.5%)</td><td>111 (27.2%)</td><td>96 (23.8%)</td><td>0.041</td></tr><tr><td>Elevated LDLc</td><td>576 (35.5%)</td><td>164 (40.5%)</td><td>144 (35.3%)</td><td>139 (34.2%)</td><td>129 (31.9%)</td><td>0.072</td></tr><tr><td>Elevated Non-HDLc</td><td>514 (31.6%)</td><td>149 (36.7%)</td><td>132 (32.4%)</td><td>117 (28.7%)</td><td>116 (28.7%)</td><td>0.043</td></tr><tr><td>Elevated ApoB</td><td>365 (22.6%)</td><td>116 (28.9%)</td><td>89 (22.0%)</td><td>83 (20.4%)</td><td>77 (19.1%)</td><td>0.004</td></tr><tr><td>Diastolic dysfunction</td><td>293 (18.4%)</td><td>79 (19.5%)</td><td>73 (18.3%)</td><td>71 (18.1%)</td><td>70 (17.6%)</td><td>0.910</td></tr><tr><td>Pulse wave velocity (m/s)</td><td>8.14 (7.28, 9.30)</td><td>8.22 (7.21, 9.48)</td><td>8.19 (7.37, 9.30)</td><td>8.16 (7.26, 9.33)</td><td>8.06 (7.28, 9.12)</td><td>0.898</td></tr><tr><td>Intima-media thickness (μm)</td><td>614.79 (522.00, 715.21)</td><td>612.17 (533.00, 706.00)</td><td>624.00 (515.96, 722.96)</td><td>613.92 (530.00, 715.00)</td><td>607.50 (510.75, 715.25)</td><td>0.594</td></tr><tr><td>Left ventricular mass (g/height<sup>2.7</sup>)</td><td>32.68 (27.51, 39.59)</td><td>33.32 (27.73, 41.15)</td><td>32.67 (27.67, 39.73)</td><td>31.92 (26.82, 38.49)</td><td>33.12 (28.07, 39.33)</td><td>0.224</td></tr></table>

Continuous variables are expressed as mean ± SD or median (Q1–Q3) as appropriate. Categorical variables are presented as n(%). BMI, body mass index; SBP, systolic blood pressure; HDL, plasma high-density cholesterol; LDL, plasma low-density cholesterol; MET, metabolic equivalent task; SBP, systolic blood pressure. Generation 2 corresponds to the youngest individuals (aged under 50 years), related to Generation 1 (aged over 50 years).
1 Kruskal-Wallis rank sum test for continuous variables; Chi2 for categorical variables with all expected cell counts ≥5 and Fisher's exact test otherwise; BH correction.

2 Milk-related products include dairy products (Milk, Yogurt, Ripened Cheese, Unripened Cheese), but also Dairy fats and Dairy desserts (cf Supplementary Table 1).
<table><caption>Table 2. Food intake characteristics according to quartiles of lipid intake from milk-related products</caption><thead><tr><th rowspan="2">Characteristics</th><th colspan="5">Quartile of lipid intake from milk-related products</th><th rowspan="2">p-value<sup>1</sup></th></tr><tr><th>Overall<br>[0-120](g/d)</th><th>Q1<br>[0-15](g/d)</th><th>Q2<br>[15-23](g/d)</th><th>Q3<br>[23-34](g/d)</th><th>Q4<br>[34-120](g/d)</th></tr></thead><tbody><tr><td>N</td><td>1632</td><td>408</td><td>408</td><td>408</td><td>408</td><td></td></tr><tr><td>Dietary lipids (g/day)</td><td>89 (67, 120)</td><td>61 (50, 77)</td><td>82 (67, 100)</td><td>97 (78, 119)</td><td>131 (109, 161)</td><td>&lt;0.001</td></tr><tr><td>Lipids from milk-related products (g/day)<sup>2,3</sup></td><td>23 (15, 34)</td><td>10 (7, 13)</td><td>19 (17, 21)</td><td>28 (25, 30)</td><td>45 (38, 55)</td><td>&lt;0.001</td></tr><tr><td>Lipids from dairy fat source (g/day)</td><td>7.11 (3.19, 13.50)</td><td>2.62 (1.46, 4.23)</td><td>6.26 (3.38, 9.75)</td><td>9.62 (4.69, 14.34)</td><td>17.17 (9.32, 24.34)</td><td>&lt;0.001</td></tr><tr><td>Lipids from dairy products (g/day)</td><td>14.00 (8.48, 22.02)</td><td>6.34 (3.86, 8.99)</td><td>12.53 (9.54, 15.58)</td><td>17.73 (13.47, 22.74)</td><td>28.69 (21.34, 37.01)</td><td>&lt;0.001</td></tr><tr><td>Dietary SFAs (g/day)</td><td>34 (25, 46)</td><td>21 (17, 26)</td><td>30 (26, 36)</td><td>38 (32, 45)</td><td>54 (46, 64)</td><td>&lt;0.001</td></tr><tr><td>SFAs from milk-related products (g/day)</td><td>14.47 (9.56, 21.07)</td><td>6.34 (4.39, 7.88)</td><td>12.05 (10.79, 13.28)</td><td>17.38 (15.82, 18.82)</td><td>27.91 (23.87, 34.28)</td><td>&lt;0.001</td></tr><tr><td>SFAs from dairy fat source (g/day)</td><td>4.34 (1.94, 8.22)</td><td>1.61 (0.89, 2.61)</td><td>3.82 (2.04, 6.02)</td><td>6.03 (2.88, 8.99)</td><td>10.59 (5.64, 15.29)</td><td>&lt;0.001</td></tr><tr><td>SFAs from dairy products (g/day)</td><td>8.75 (5.38, 13.86)</td><td>3.96 (2.41, 5.70)</td><td>7.91 (6.00, 9.82)</td><td>11.13 (8.44, 14.25)</td><td>17.98 (13.33, 23.13)</td><td>&lt;0.001</td></tr><tr><td>SFAs from Milk (g/day)</td><td>0.08 (0.00, 1.19)</td><td>0.00 (0.00, 0.25)</td><td>0.07 (0.00, 1.19)</td><td>0.13 (0.00, 1.19)</td><td>0.25 (0.00, 1.36)</td><td>&lt;0.001</td></tr><tr><td>SFAs from Yogurt (g/day)</td><td>1.15 (0.24, 2.94)</td><td>0.33 (0.05, 1.27)</td><td>1.15 (0.35, 2.41)</td><td>1.57 (0.36, 3.06)</td><td>2.41 (0.63, 4.83)</td><td>&lt;0.001</td></tr><tr><td>SFAs from Cheese (g/day)</td><td>6.16 (3.25, 10.45)</td><td>2.58 (1.48, 3.99)</td><td>5.39 (3.26, 7.64)</td><td>7.84 (5.44, 10.98)</td><td>13.38 (8.73, 18.54)</td><td>&lt;0.001</td></tr><tr><td>SFAs from Unripened cheese (g/day)</td><td>0.94 (0.37, 2.22)</td><td>0.58 (0.19, 1.12)</td><td>0.90 (0.39, 2.00)</td><td>1.17 (0.47, 2.50)</td><td>1.57 (0.54, 4.34)</td><td>&lt;0.001</td></tr><tr><td>SFAs from Ripened cheese (g/day)</td><td>4.35 (1.82, 8.33)</td><td>1.67 (0.72, 2.82)</td><td>3.58 (1.93, 5.80)</td><td>5.90 (3.12, 8.92)</td><td>10.33 (5.39, 16.69)</td><td>&lt;0.001</td></tr><tr><td>SFAs from Cream (g/day)</td><td>0.81 (0.32, 1.44)</td><td>0.41 (0.13, 0.81)</td><td>0.73 (0.32, 1.09)</td><td>1.00 (0.32, 1.90)</td><td>1.03 (0.41, 2.76)</td><td>&lt;0.001</td></tr><tr><td>SFAs from Butter (g/day)</td><td>1.31 (0.00, 5.23)</td><td>0.18 (0.00, 1.12)</td><td>1.12 (0.17, 2.99)</td><td>2.62 (0.18, 5.23)</td><td>5.23 (1.68, 10.46)</td><td>&lt;0.001</td></tr><tr><td>SFAs from Dairy dessert (g/day)</td><td>0.79 (0.12, 1.78)</td><td>0.36 (0.00, 0.92)</td><td>0.79 (0.12, 1.56)</td><td>0.96 (0.34, 2.01)</td><td>1.19 (0.45, 2.96)</td><td>&lt;0.001</td></tr></tbody></table>

Variables are expressed as median (Q1–Q3).

<footnote n="1">¹ Kruskal-Wallis rank sum test for continuous variables; Chi2 for categorical variables with all expected cell counts ≥5 and Fisher's exact test otherwise; BH correction.</footnote>

<footnote n="2">² Milk-related products include dairy products (Milk, Yogurt, Ripened Cheese, Unripened Cheese), but also Dairy fats, Dairy desserts (cf Supplementary Table 1).</footnote>

<footnote n="3">³ Among these lipids, SFAs represents 62.3%.</footnote>
<table><caption>Table 3. Association between milk-related lipids and SFAs and left ventricular mass.</caption><thead><tr><th rowspan="3">Left ventricular mass</th><th colspan="3">Whole cohort</th><th colspan="3">Restricted cohort</th><th colspan="3">Restricted cohort with adjustment for physical activity</th></tr><tr><th colspan="3">Exp(β) [95% CI], P-value</th><th colspan="3">Exp(β) [95% CI], P-value</th><th colspan="3">Exp(β) [95% CI], P-value</th></tr><tr><th>Mod1</th><th>Mod2</th><th>Mod3</th><th>Mod1</th><th>Mod2</th><th>Mod3</th><th>Mod1</th><th>Mod2</th><th>Mod3</th></tr></thead><tbody><tr><td>Total dietary lipids</td><td>1.014 [0.984<br/>-1.046],<br/>p= 0.4</td><td>0.967 [0.907<br/>-1.030],<br/>p= 0.3</td><td>0.979 [0.920<br/>-1.042],<br/>p= 0.5</td><td>1.019<br/>[0.985 -<br/>1.054],<br/>p= 0.3</td><td>0.979 [0.913<br/>-1.050],<br/>p= 0.6</td><td>0.991 [0.924<br/>-1.062],<br/>p= 0.8</td><td>1.019 [0.985<br/>-1.054],<br/>p=0.3</td><td>0.976 [0.910<br/>-1.047],<br/>p= 0.5</td><td>0.987 [0.921<br/>-1.058],<br/>p= 0.7</td></tr><tr><td>Total dietary SFAs</td><td>1.003 [0.930<br/>-1.083],<br/>p>0.9</td><td>0.820 [0.712<br/>-0.945],<br/>p=0.006</td><td>0.849 [0.739<br/>-0.976],<br/>p= 0.022</td><td>1.018<br/>[0.935 -<br/>1.108],<br/>p= 0.7</td><td>0.874 [0.747<br/>-1.023],<br/>p= 0.093</td><td>0.909 [0.778<br/>-1.062],<br/>p= 0.2</td><td>1.018 [0.935<br/>-1.108],<br/>p= 0.7</td><td>0.868 [0.742<br/>-1.016],<br/>p= 0.077</td><td>0.903 [0.773<br/>-1.054],<br/>p= 0.2</td></tr><tr><td>SFAs from dairy fat sources</td><td>0.998 [0.995<br/>-1.000],<br/>p=0.10</td><td>0.998 [0.995<br/>-1.000],<br/>p= 0.073</td><td>0.998 [0.996<br/>-1.001],<br/>p= 0.2</td><td>0.998<br/>[0.995 -<br/>1.001],<br/>p= 0.2</td><td>0.999 [0.996<br/>-1.001],<br/>p= 0.3</td><td>0.999 [0.996<br/>-1.002],<br/>p= 0.5</td><td>0.998 [0.995<br/>-1.001],<br/>p= 0.2</td><td>0.998 [0.996<br/>-1.001],<br/>p= 0.3</td><td>0.999 [0.996<br/>-1.002],<br/>p= 0.4</td></tr><tr><td>SFAs from dairy products</td><td>0.999 [0.997<br/>-1.000],<br/>p= 0.14</td><td>0.997 [0.996<br/>-0.999],<br/>p= 0.006</td><td>0.997 [0.996<br/>-0.999],<br/>p= 0.006</td><td>0.999<br/>[0.997 -<br/>1.001],<br/>p= 0.4</td><td>0.998 [0.996<br/>-1.000],<br/>p= 0.10</td><td>0.998 [0.996,<br/>1.000],<br/>p= 0.13</td><td>0.999 [0.997<br/>-1.001],<br/>p= 0.4</td><td>0.998 [0.996<br/>-1.000],<br/>p= 0.10</td><td>0.998 [0.996<br/>-1.000],<br/>p= 0.13</td></tr><tr><td>SFAs from Butter</td><td>0.998 [0.995<br/>-1.000],<br/>p= 0.10</td><td>0.998<br/>[0.995,<br/>1.000],<br/>p= 0.073</td><td>0.998<br/>[0.996,<br/>1.001],<br/>p= 0.2</td><td>0.998<br/>[0.995 -<br/>1.001],<br/>p= 0.2</td><td>0.999 [0.996<br/>-1.001],<br/>p= 0.3</td><td>0.999 [0.996<br/>-1.002],<br/>p= 0.5</td><td>0.998 [0.995<br/>-1.001],<br/>p= 0.2</td><td>0.998 [0.996<br/>-1.001],<br/>p= 0.3</td><td>0.999 [0.996<br/>-1.002],<br/>p= 0.4</td></tr><tr><td>SFAs from Cream</td><td>1.000 [0.994<br/>-1.007],<br/>p>0.9</td><td>0.998 [0.991<br/>-1.004],<br/>p=0.5</td><td>0.998 [0.991<br/>-1.004],<br/>p= 0.5</td><td>1.000 [0.992<br/>-1.007],<br/>p>0.9</td><td>0.997 [0.990<br/>-1.004],<br/>p= 0.4</td><td>0.997 [0.99 -<br/>1.004],<br/>p= 0.4</td><td>1.000 [0.992<br/>-1.007],<br/>p>0.9</td><td>0.997 [0.990<br/>-1.004],<br/>p= 0.4</td><td>0.997 [0.990,<br/>1.004],<br/>p=0.4</td></tr><tr><td>SFAs from Dairy dessert</td><td>1.001 [0.994<br/>-1.008],<br/>p= 0.8</td><td>1.001 [0.994<br/>-1.008],<br/>p= 0.8</td><td>1.001 [0.995<br/>-1.008],<br/>p= 0.7</td><td>1.002<br/>[0.994-<br/>1.009],<br/>p= 0.6</td><td>1.001 [0.994<br/>-1.009],<br/>p= 0.8</td><td>1.001 [0.994<br/>-1.009],<br/>p= 0.7</td><td>1.002 [0.994<br/>-1.009],<br/>p= 0.6</td><td>1.001 [0.994<br/>-1.008],<br/>p= 0.8</td><td>1.001 [0.994<br/>-1.009],<br/>p= 0.7</td></tr></tbody></table>
<table><tbody><tr><td>SFAs from Milk</td><td>0.995 [0.987<br/>- 1.003],<br/>p= 0.2</td><td>0.996 [0.988<br/>- 1.003],<br/>p= 0.3</td><td>0.996 [0.989<br/>- 1.003],<br/>p= 0.3</td><td>0.993 [0.984<br/>- 1.002]<br/>p= 0.12</td><td>0.994<br/>[0.986<br/>- 1.003],<br/>p= 0.2</td><td>0.995 [0.987<br/>- 1.004],<br/>p= 0.3</td><td>0.993 [0.984<br/>- 1.002],<br/>p= 0.12</td><td>0.994 [0.986<br/>- 1.002],<br/>p= 0.2</td><td>0.995 [0.987<br/>- 1.003],<br/>p= 0.3</td></tr><tr><td>SFA from Ripened<br/>cheese</td><td>0.999 [0.997<br/>- 1.001],<br/>p= 0.4</td><td>0.998 [0.996<br/>- 1.000],<br/>p= 0.083</td><td>0.998 [0.996<br/>- 1.000],<br/>p= 0.11</td><td>1.000<br/>[0.997 -<br/>1.002],<br/>p= 0.8</td><td>0.999 [0.996<br/>- 1.001],<br/>p= 0.4</td><td>0.999 [0.997<br/>- 1.002],<br/>p= 0.5</td><td>1.000 [0.997<br/>- 1.002],<br/>p= 0.8</td><td>0.999 [0.997<br/>- 1.001],<br/>p= 0.4</td><td>0.999 [0.997<br/>- 1.002],<br/>p= 0.5</td></tr><tr><td>SFAs from<br/>Unripened cheese</td><td>1.001 [0.996<br/>- 1.006],<br/>p= 0.7</td><td>0.998 [0.993<br/>- 1.003],<br/>p= 0.5</td><td>0.996 [0.991<br/>- 1.001],<br/>p= 0.2</td><td>1.001<br/>[0.995 -<br/>1.007],<br/>p= 0.9</td><td>0.998 [0.992<br/>- 1.004],<br/>p= 0.5</td><td>0.997 [0.991<br/>- 1.003],<br/>p= 0.3</td><td>1.001 [0.995<br/>- 1.007],<br/>p= 0.9</td><td>0.998 [0.993<br/>- 1.004],<br/>p= 0.6</td><td>0.997 [0.991<br/>- 1.003],<br/>p= 0.3</td></tr><tr><td>SFAs from Yogurt</td><td>0.995 [0.990<br/>- 0.999],<br/>p= 0.040</td><td>0.995 [0.990<br/>- 0.999],<br/>p= 0.045</td><td>0.996 [0.991<br/>- 0.999],<br/>p= 0.072</td><td>0.997<br/>[0.991 -<br/>1.002],<br/>p= 0.2</td><td>0.997 [0.992<br/>- 1.003],<br/>p= 0.4</td><td>0.998 [0.993<br/>- 1.003],<br/>p= 0.5</td><td>0.997 [0.991<br/>- 1.002],<br/>p= 0.2</td><td>0.997 [0.992<br/>- 1.003],<br/>p= 0.3</td><td>0.998 [0.993<br/>- 1.003],<br/>p= 0.4</td></tr></tbody></table>

Exp(β) is the multiplicative factor for each 1g/day increase in lipids or SFAs. It is held that 100 x (exp(β) - 1) is the percent change in left ventricular mass associated with a 1g/day increase of lipids or SFA.

SFAs, saturated fatty acids. Each milk-related product types which are sources of SFAs are defined in Supplementary Table 1. For adjustment level 1, the models were adjusted for age and sex. For level 2, the adjustment was extended to waist circumference, tobacco use, education level, DASH score, number of ultra-processed food servings, total energy intake and alcohol intake. For level 3, the models were additionally adjusted for the use of antihypertensive, antidiabetic, and lipid-lowering drugs, as well as adherence to a diet.
Figure legends

<figure><img src="image_2.png" /><figcaption>Figure 1. Flowchart of the study</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>Figure 2. Contribution of lipids from each dairy item to total milk-related lipid intake across quartiles.</figcaption></figure>

expressed in g/d for each item, (B) expressed as % of total milk-related lipid intake for each product type. Each milk-related product types are
defined in Supplementary Table 1.
<figure><img src="image_4.png" /><figcaption>Figure 3. Contribution of each food group to lipid intake across quartiles of lipid intake according to generation.</figcaption></figure>

Generation 2 corresponds to the youngest individuals (aged under 50 years), and Generation 1 the oldest individuals (aged over 50 years). Each milk-related product types are defined in Supplementary Table 1.
<figure><img src="image_5.png" /><figcaption>Figure 4. Associations between lipids as well as SFA and four different hyperlipidemia outcomes.</figcaption></figure>
Results are expressed for each increment of 1 g/day of lipids and SFAs. SFAs, saturated fatty acids. Each milk-related product types which are sources of SFAs are defined in Supplementary Table 1. A) Hypertriglyceridemia, defined as triglycerides ≥1.5 g/L or treated by lipid-lowering drugs, B) Elevated LDLc, defined as LDLc ≥ 1.6 g/L or treated by lipid-lowering drugs, C) elevated non-HDLc defined as non-HDLc ≥ 1.9 g/L or treated by lipid-lowering drugs, D) elevated ApoB defined as ApoB ≥ 1.3 g/L or treated by lipid-lowering drugs. For adjustment level 1, the models were adjusted for age and sex. For level 2, the adjustment was extended to waist circumference, tobacco use, education level, DASH score, number of ultra-processed food servings), total energy intake and alcohol intake. For level 3, the models were additionally adjusted for the use of antihypertensive and antidiabetic drugs, as well as adherence to a diet.
<figure><img src="image_6.png" /><figcaption>Figure 5. Associations between SFA from seven types of milk-related products and hypertriglyceridemia.</figcaption></figure>
Results are expressed for each increment of 1 g/day of lipids and SFAs. SFAs, saturated fatty acids. Each milk-related product types which are sources of SFA are defined in Supplementary Table 1. Hypertriglyceridemia was defined as triglycerides ≥1.5 g/L or treated by lipid-lowering drugs A) for the whole cohort, B) for the restricted cohort, C) for the restricted cohort with adjustment for physical activity. For adjustment level 1, the models were adjusted for age and sex. For level 2, the adjustment was extended to waist circumference, tobacco use, education level, DASH score, number of ultra-processed food servings, total energy intake and alcohol intake. For level 3, the models were additionally adjusted for the use of antihypertensive and antidiabetic as well as adherence to a diet (and physical activity in panel C only).
<figure><img src="image_7.png" /><figcaption>Figure 6. Associations between SFA from seven types of milk-related products and elevated LDLc.</figcaption></figure>
The results are expressed for each increment of 1 g/day of **lipids and SFAs**. SFA, saturated fatty acids. Each milk-related products types which are source of SFA are defined in Supplementary Table 1. A) for the full cohort, B) for the restricted cohort, C) for the restricted cohort with adjustment for physical activity. Elevated LDLc was defined as LDL-cholesterol≥1.6 g/L or treated by lipid-lowering drugs. For adjustment level 1, the models were adjusted for age and sex. For level 2, the adjustment was extended to waist circumference, tobacco use, diploma, DASH score, number of ultra-processed foods servings, total energy intake and alcohol intake. For level 3, the models were adjusted taking also into account the use of antihypertensive and antidiabetic as well as adherence to a diet (and physical activity in panel C only).
<figure><img src="image_8.png" /><figcaption>Figure 7. Associations between SFA from seven types of milk-related products and elevated non-HDLc.</figcaption></figure>
The results are expressed for each increment of 1 g/day of lipids and SFA. SFA, saturated fatty acids. Each milk-related products types which are source of SFA are defined in Supplementary Table 1. Elevated non-HDL-cholesterol as non-HDL-cholesterol ≥1.9 g/L or treated by lipid-lowering drugs. A) for the full cohort, B) for the restricted cohort, C) for the restricted cohort with adjustment for physical activity. For adjustment level 1, the models were adjusted for age and sex. For level 2, the adjustment was extended to waist circumference, tobacco use, diploma, DASH score, number of ultra-processed foods servings, total energy intake and alcohol intake. For level 3, the models were adjusted taking also into account the use of antihypertensive and antidiabetic as well as adherence to a diet (and physical activity in panel C only).
<figure><img src="image_9.png" /><figcaption>Figure 8. Associations between SFA from seven types of milk-related products and elevated ApoB.</figcaption></figure>
The results are expressed for each increment of 1 g/day of lipids and SFA. SFA, saturated fatty acids. Each milk-related products types which are source of SFA are defined in Supplementary Table 1. Elevated ApoB as ApoB≥1.3 g/L or treated by lipid-lowering drugs. A) for the full cohort, B) for the restricted cohort, C) for the restricted cohort with adjustment for physical activity. For adjustment level 1, the models were adjusted for age and sex. For level 2, the adjustment was extended to waist circumference, tobacco use, diploma, DASH score, number of ultra-processed foods servings, total energy intake and alcohol intake. For level 3, the models were adjusted taking also into account the use of antihypertensive and antidiabetic as well as adherence to a diet (and physical activity in panel C only).
<figure><img src="image_10.png" /><figcaption>Figure 9. Associations between milk-related lipids and SFA and metabolic syndrome.</figcaption></figure>
Results are expressed for each increment of 1 g/day of lipids and SFAs. SFAs, saturated fatty acids. Each milk-related product types which are sources of SFA are defined in Supplementary Table 1. For adjustment level 1, the models were adjusted for age and sex. For level 2, the adjustment was extended to tobacco use, education level, DASH score, number of ultra-processed food servings, total energy intake and alcohol intake. For level 3, the models were additionally adjusted for the use of antihypertensive, antidiabetic, and lipid-lowering drugs, as well as adherence to a diet.
<figure><img src="image_11.png" /><figcaption>Figure 10. Associations between milk-related lipids and SFAs and diabetes/prediabetes.</figcaption></figure>
Results are expressed for each increment of 1 g/day of lipids and SFAs. SFAs, saturated fatty acids. Each milk-related product types which are sources of SFA are defined in Supplementary Table 1. For adjustment level 1, the models were adjusted for age and sex. For level 2, the adjustment was extended to waist circumference, tobacco use, education level, DASH score, number of ultra-processed food servings, total energy intake and alcohol intake. For level 3, the models were additionally adjusted for the use of antihypertensive, antidiabetic, and lipid-lowering drugs, as well as adherence to a diet.
<figure><img src="image_12.png" /><figcaption>Figure 11. Association between milk-related lipids and SFAs and diastolic dysfunction.</figcaption></figure>
Results are expressed for each increment of 1 g/day of lipids and SFAs. SFAs, saturated fatty acids. Each milk-related product types which are a source of SFA are defined in Supplementary Table 1. For adjustment level 1, the models were adjusted for age and sex. For level 2, the adjustment was extended to waist circumference, tobacco use, education level, DASH score, number of ultra-processed food servings, total energy intake and alcohol intake. For level 3, the models were additionally adjusted for the use of antihypertensive, antidiabetic, and lipid-lowering drugs, as well as adherence to a diet.
Graphical abstract

STANISLAS cohort
1619 participants
in France

Neutral to beneficial associations of
saturated fatty acids from dairy products
and dairy fat sources
with cardio-metabolic health
(metabolic syndrome, diabetes/prediabetes,
hyperlipidemia, cardiovascular damage)

<figure><img src="image_13.png" /></figure>

DAILICATE project

**Saturated fatty acid intake from:**

Dairy

<figure><img src="image_14.png" /></figure>

Inverse association
with:

Left ventricular mass

<figure><img src="image_15.png" /></figure>

Butter

<figure><img src="image_16.png" /></figure>

Hyperlipidemia

<figure><img src="image_17.png" /></figure>

* elevated TG

* elevated LDLc,

* elevated non-HDLc

* elevated ApoB

Yogurt

<figure><img src="image_18.png" /></figure>

Elevated ApoB

Icon design by macrovector/Freepik and pch vector/Freepik

Wagner, Girerd et al. 2025 European Journal of Nutrition - 10.1007/s00394-025-03763-1