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Background: Precision prevention involves tailoring interventions to the unique characteristics of a group or individual to maximize their effectiveness. In this study, we examined the role of participant characteristics in the effectiveness of lifestyle interventions to optimize gestational weight gain (GWG). Methods: We searched Medline, Embase, and PubMed, from inception up to March 2025, to identify randomized and non-randomized controlled trials of lifestyle interventions (diet, physical activity, or combined) commencing before or during pregnancy. Participant characteristics, including age, race/ethnicity, body mass index (BMI), employment status, fasting low- and high-density lipoprotein cholesterol (HDL-C) were assessed. Mean differences (MD) in GWG were pooled using the random-effect model. Meta-regression and subgroup analysis were conducted by participant characteristics (e.g., BMI). Results: A total of 86 studies with 28,270 participants were included in this systematic review and meta-analysis. All lifestyle intervention types significantly reduced GWG. Combined lifestyle interventions initiated at first (MD −0.68; 95% confidence interval [CI]: −1.28, −0.07) and early second (13–17 weeks) trimester (MD −0.83; 95% CI: −1.46, −0.20) provide better effectiveness in optimizing GWG. Diet-only interventions significantly reduced GWG only in participants with normal BMI (MD −1.33 kg; CI: −1.75, −1.91) compared to the other BMI categories. Combined diet and physical activity interventions reduce excessive GWG in women with higher baseline HDL-C (β −0.04; 95% CI −0.06, −0.01). Conclusions: Lifestyle interventions reduced excessive GWG, with possible differential effects by intervention initiation time, BMI, and HDL-C. Future studies should consider physiological as well as social characteristics, in line with a holistic framework for precision medicine.
Participant characteristics in the effectiveness of lifestyle interventions to optimize gestational weight gain: a systematic review and meta-analysis
Grieger J. A.;Takele W. W.;Vesco K. K.;Redman L. M.;Hannah W.;Bonham M. P.;Chen M.;Chivers S. C.;Fawcett A. J.;Habibi N.;Liu K.;Mekonnen E. G.;Pathirana M.;Quinteros A.;Taylor R.;Ukke G. G.;Zhou S. J.;Franks P. W.;Rich S. S.;Wagner R.;Vilsboll T.;Udler M. S.;Tuomi T.;Sweeting A.;Sims E. K.;Sherr J. L.;Semple R. K.;Reynolds R. M.;Redondo M. J.;Pratley R. E.;Pop-Busui R.;Pollin T. I.;Perng W.;Pearson E. R.;Ozanne S. E.;Owen K. R.;Oram R.;Murphy R.;Mohan V.;Misra S.;Meigs J. B.;Mathioudakis N.;Mathieu C.;Ma R. C. W.;Loos R. J. F.;Lim S. S.;Laffel L. M.;Kwak S. H.;Josefson J. L.;Hood K. K.;Hivert M. -F.;Hirsch I. B.;Hattersley A. T.;Griffin K.;Greeley S. A. W.;Gottlieb P. A.;Gomez M. F.;Gloyn A. L.;Florez J. C.;Dennis J. M.;Costacou T.;Boyle K.;Billings L. K.;Brown R. J.;Philipson L. H.;Nolan J. J.;Eckel R. H.;Sherifali D.;Mixter E.;Gruber C.;Fawcett A. J.;de Souza R.;Auh S.;Zhu Y.;Zhang C.;Saint-Martin C.;Provenzano M.;Pomares-Millan H.;Njolstad P. R.;Nakabuye M.;Molnes J.;McGovern A.;Maloney K. A.;Flanagan S. E.;de Franco E.;Aukrust I.;Polak M.;Beltrand J.;Zhang Y.;Yu G.;White S. L.;Hannah W.;Wentworth J. M.;Vatier C.;Van der Schueren B.;Urazbayeva M.;Ukke G. G.;Tye S. C.;Stoy J.;Stefan N.;Steck A. K.;Steenackers N.;Stanislawski M. A.;Speake C.;Sheu W. H. -H.;Selvin E.;Scholtens D. M.;Monaco G. S. F.;Sarkar S.;Kanbour S.;Santhakumar V.;Saeed Z.;Ried-Larsen M.;Ray D.;Jain R.;Powe C. E.;Petrie J. R.;Perez D.;Pazmino S.;Pankow J. S.;Onengut-Gumuscu S.;Motala A. A.;Morton R. W.;Lowe W. L.;Long S. A.;Libman I. M.;Leung G. K. W.;Leong A.;Koivula R. W.;Jones A. G.;Johnson R. K.;Hoag B.;Ismail H. M.;Harris-Kawano A.;Huang C.;Hansen T.;Guasch-Ferre M.;Grieger J. A.;Goodarzi M. O.;Gitelman S. E.;Fitzpatrick S. L.;Fitipaldi H.;Fernandez-Balsells M. M.;Evans-Molina C.;Dudenhoffer-Pfeifer M.;DiMeglio L. A.;Dickens L. T.;Deutsch A. J.;Dawed A. Y.;Dabelea D.;Clemmensen C.;Chivers S. C.;Chikowore T.;Cheng F.;Riswa M.;Andersen M. K.;Amouyal C.;Young K.;Yamamoto J. M.;Wong J. J.;Wang C. C.;Wallace A. S.;Tosur M.;Thuesen A. C. B.;Tam C. H. -T.;Svalastoga P.;Sevilla-Gonzalez M.;Semnani-Azad Z.;Schon M.;Rooney M. R.;Raghavan S.;Prystupa K.;Pilla S. J.;Patel K. A.;Ozkan B.;Naylor R. N.;Most J.;Morieri M. L.;Miller R. G.;Mclennan N. -M.;Massey R.;Mannisto J. M. E.;Lim L. -L.;Kreienkamp R. J.;Kettunen J. L. T.;Kahkoska A. R.;Jacobsen L. M.;Ikle J. M.;Hughes A.;Haider E.;Gaillard R.;Gingras V.;Gillard P.;Francis E. C.;Felton J. L.;Duan D.;Cromer S. J.;Corcoy R.;Colclough K.;Clark A. L.;Bodhini D.;Benham J. L.;Aiken C.;Ahmad A.;Merino J.;Tobias D. K.;Josefson J.;Lim S.
2025-01-01
Abstract
Background: Precision prevention involves tailoring interventions to the unique characteristics of a group or individual to maximize their effectiveness. In this study, we examined the role of participant characteristics in the effectiveness of lifestyle interventions to optimize gestational weight gain (GWG). Methods: We searched Medline, Embase, and PubMed, from inception up to March 2025, to identify randomized and non-randomized controlled trials of lifestyle interventions (diet, physical activity, or combined) commencing before or during pregnancy. Participant characteristics, including age, race/ethnicity, body mass index (BMI), employment status, fasting low- and high-density lipoprotein cholesterol (HDL-C) were assessed. Mean differences (MD) in GWG were pooled using the random-effect model. Meta-regression and subgroup analysis were conducted by participant characteristics (e.g., BMI). Results: A total of 86 studies with 28,270 participants were included in this systematic review and meta-analysis. All lifestyle intervention types significantly reduced GWG. Combined lifestyle interventions initiated at first (MD −0.68; 95% confidence interval [CI]: −1.28, −0.07) and early second (13–17 weeks) trimester (MD −0.83; 95% CI: −1.46, −0.20) provide better effectiveness in optimizing GWG. Diet-only interventions significantly reduced GWG only in participants with normal BMI (MD −1.33 kg; CI: −1.75, −1.91) compared to the other BMI categories. Combined diet and physical activity interventions reduce excessive GWG in women with higher baseline HDL-C (β −0.04; 95% CI −0.06, −0.01). Conclusions: Lifestyle interventions reduced excessive GWG, with possible differential effects by intervention initiation time, BMI, and HDL-C. Future studies should consider physiological as well as social characteristics, in line with a holistic framework for precision medicine.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/397798
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simulazione ASN
Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2023-2025 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
La presente simulazione è stata realizzata sulla base delle specifiche raccolte sul tavolo ER del Focus Group IRIS coordinato dall’Università di Modena e Reggio Emilia e delle regole riportate nel DM 589/2018 e allegata Tabella A. Cineca, l’Università di Modena e Reggio Emilia e il Focus Group IRIS non si assumono alcuna responsabilità in merito all’uso che il diretto interessato o terzi faranno della simulazione. Si specifica inoltre che la simulazione contiene calcoli effettuati con dati e algoritmi di pubblico dominio e deve quindi essere considerata come un mero ausilio al calcolo svolgibile manualmente o con strumenti equivalenti.
