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Optimal strategies to prevent CKM-related outcomes are greatly needed. We assessed the association between various dietary patterns and CKM biomarkers in a general population study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We used cross-sectional data from 8066 adult participants of the Cooperative Health Research in South Tyrol (CHRIS) study. Self-reported dietary intake was assessed through the semi-quantitative GA\n                      <jats:sup>2<\/jats:sup>\n                      LEN Food Frequency questionnaire. We derived six established dietary pattern indices, and population-specific dietary patterns through principal component analysis. CKM biomarkers included: systolic and diastolic blood pressure, glycated haemoglobin, visceral fat, HDL, LDL and total cholesterol, triglycerides, creatinine-based estimated glomerular filtration rate, and urinary albumin-to-creatinine ratio. We investigated associations between each dietary index and CKM risk marker using multivariable linear regression models. We further examined potential effect modification by sex, body mass index, physical activity and smoking habit.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Higher adherence to healthy dietary patterns rich in fruits, vegetables, whole grains, and low in saturated fats was associated with more favorable CKM marker levels. E.g, when comparing the fourth to the first quartile the healthy plant-based dietary index was associated with lower total and LDL cholesterol (\u03b2\u00a0\u2248\u00a0\u2212\u00a0\u20095.0\u00a0mg\/dL,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a0\u20090.001), lower blood pressure (\u03b2=\u00a0\u2212\u00a01.24 mmHg,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.001) and higher eGFR (\u03b2\u2009=\u20091.24\u00a0ml\/min\/1.73m\n                      <jats:sup>2<\/jats:sup>\n                      ,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.001). In contrast, the unhealthy plant based and a Western style diet were associated with higher SBP (\u03b2\u00a0\u2009=\u00a0\u20091.23\u20131.89\u00a0mmHg,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.01), amongst others. For some markers, associations tended to be stronger in men, individuals with overweight or obesity, those with lower physical activity, and smokers (P\n                      <jats:sub>interaction<\/jats:sub>\n                      &lt;\u00a00.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Adhering to a healthy plant rich diet low in saturated fats, added sugars and processed foods was associated with more favorable levels of CKM biomarkers, including blood lipids, blood pressure, glycated hemoglobin and eGFR.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00394-026-03992-y","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T13:51:55Z","timestamp":1780062715000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Associations between dietary patterns and cardiovascular, kidney and metabolic risk markers in the Cooperative Health Research in South Tyrol study"],"prefix":"10.1007","volume":"65","author":[{"given":"Essi","family":"Hantikainen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophie","family":"Berger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giulia","family":"Barbieri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter P.","family":"Pramstaller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vanessa","family":"Garcia-Larsen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"G\u00f6gele","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristian","family":"Pattaro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rebecca","family":"Lundin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisco S.","family":"Domingues","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,29]]},"reference":[{"key":"3992_CR1","doi-asserted-by":"publisher","first-page":"2385","DOI":"10.1097\/JS9.0000000000002260","volume":"111","author":"J Li","year":"2025","unstructured":"Li J, Pandian V, Davidson PM, Song Y, Chen N, Fong DYT (2025) Burden and attributable risk factors of non-communicable diseases and subtypes in 204 countries and territories, 1990\u20132021: a systematic analysis for the global burden of disease study 2021. 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