Social connections are differentially related to subjective age and physiological age acceleration amongst older adults

Domain: neuroscience
Published: 2026-01-30
Editorial outcome: minor_revision

Abstract
Humansocialconnectionsarecomplexecosystemsformedofstructural, functionalandqualitycomponents.Weaksocialconnectionsareassociated withadverseage-relatedhealthoutcomes,butweknowlittleabouttheageing- relatedprocessesunderlyingthis.Usingdatafrom7047adultsaged50+inthe EnglishLongitudinalStudyofAgeing,weexploreassociationsbetweendiverse aspectsofsocialconnectionsandbotholdersubjectiveageandaccelerated physiologicalageusingavalidatedphysiologicalageingcombiningcardio- vascular,respiratory,haematologicandmetabolicindicators.Doublyrobust estimationsusinginverse-probability-weightedregressionadjustmentesti- matorsshowthatlivingalone,lowsocialintegrationandlowsocialsupportare riskfactorsforphysiologicalageacceleration.However,weaksocialconnec- tionsdidnothaveastatisticallysignificantassociationwitholdersubjective age.Analysesarerobusttomultiplesensitivityanalysesandmaintainedfour yearslater.Weproposethehypothesisthatacceleratedphysiologicalageing maybeamechanismunderpinningtherelationshipbetweenweaksocial connectionsandage-relatedmorbitidyandmortality.

Introduction
Humansocialconnectionsarecomplexecosystemsformedofstructural components(suchasthesizeofsocialnetworksandfrequencyofsocial contact), functional components (including the social and emotional supportprovidedbythesenetworksandcontacts)andqualitycompo- nents(includingthepositiveandnegativeexperiencesourinteractions bring)1. Multi-national research demonstrates that social connections changeoverthelife-course,withindividualsprioritisingcertaintypesof social goals at different life points,such as focusing more on quality ratherthanquantityofsocialconnectionsastheyage.However,there arecertainpatternsthatarewell-evidencedandrecognisedasindicative notjustofadaptiveofcompensatorypatternsbutinsteadofweaksocial connectionsthatcanbedetrimentaltohealth2.Forexample,estimates oftherelationshipbetweenlonelinessandageshowU-shapedcurves, withlonelinessbeinghigheramongstyoungerpeoplebutalsosteadily increasing from around age 60 onwards3,4. Similarly, social isolation becomes more prominent as people age, with severe social isolation being4timesmorelikelyinthoseaged>90comparedtoaged65–695.

Theseweaksocialconnectionsareclearlyandstronglyassociatedwith adversehealthoutcomes,includingtheincidenceofphysicaldiseases (e.g.cardiovasculardisease/dementia/diabetes)6–9,psychiatricdisorders (e.g.depression/anxiety/schizophrenia)10,11,age-relateddecline12,13,and mortality(throughsuicideandothercauses)14–16.

Recentbiologicalresearchhasidentifiedanumberofmechanistic pathwaysthatlinkweaksocialconnectionstotheseage-relatedout- comes,including(i)anoveractiveHPAaxisresultinginreceptorcells developing glucocorticoid resistance, leading to a greater suscept- ibility to inflammation, (ii) dysregulation of the autonomic nervous system’s ability to regular cardiovascular activity, (iii) maladaptive changes in immunological responses, (iv) reduced repair and restorative processes, and (v) increased brain atrophy and reduced neurogenesis17–19.Thesebiologicalprocessesarenotablebecausethey themselves include important hallmarks of ageing, which do not necessarily advancein tandem with chronological age20. Thesepro- cesseshavelargelybeenlookedatinisolation,withstudiesfocusingon individual biomarkers21–24, but research into physiological ageing indicesisprovidingalternativeavenuesforexploration.Physiological ageing indices combine phenotypic measures of age-related blood- based biomarkers (e.g. fibrinogen, C-reactive protein and glycated haemoglobin)withtestsofbroaderage-relatedphysiologicalfunction (e.g. grip strength, respiratory function, pulse pressure and blood pressure)25–27. Acceleration of physiological age relative to chron- ologicalagehasbeenshowntopredictmultipleage-relatedoutcomes including cardiovascular disease, arthritis, osteoporosis, cognitive dysfunction, dementia and mortality25–27. However, to date, there is littleresearchinto whether weaksocialconnections relate to lower physiologicalageusingsuchindices.

Inadditiontoinfluencingphysiologicalprocessesofageing,itis alsoplausiblethatculturalengagementhelpsindividualstomaintaina feeling of youthfulness, reducing their perceived or subjective age. Subjective age (asking an individual how old they feel) is a simple assessment of a complex phenomenon. How old someone feels is sociallyand culturally laden, reflecting factors suchassocialnorms andpersonaldesires.Butitalsoreflectspsychologicalandphysiolo- gicalprocesses,andtheseprocessesarerelatedtomaintaininggood healthandphysicalfunction.Psychologically,theseprocessesinclude feelings of vitality, wellbeing, optimism and self-esteem28,29, all of whicharethemselvesrelatedtolowerriskofage-relatedmorbidityand mortality30.Physiologically,theseprocessesincludeaperson’ssense oftheirownfunctioningandcapacity.Notably,subjectiveagecanbe animportantmarkerinitsownright:peoplewhofeelyoungerthan theyarechronologicallyhavelowerinflammation,acceleratedepige- neticageing,betterpreservationofcognition,reducedriskoffuture dementia,fewerfuturehospitalisations,andlowermortalityrisk,even aftercontrollingforexistinghealthstatus28,31–37.Butwhereasthereisa strong literature on how subjective age relates to age-related mor- bidityandmortality,thereismuchlessresearchintofactorsassociated withanoldersubjectiveage,suchasweaksocialconnections.

Consequently,subjectiveandphysiologicalageprovidedifferent butcomplementaryinsightintomechanisticprocessesthatcouldlink weaksocialconnectionstoage-relatedmorbidityandmortality.Inthis paper, we take a first step towards testing such a mechanism by exploring whether there are associations between weak social con- nectionsandoldersubjectiveageandacceleratedphysiologicalage. Assessingbothsubjectiveandphysiologicalageinparallelisimportant as it provides the opportunity to explorewhether correlations with weaksocialconnectionsmaybestrongerforsubjectiveorobjective markers. We used a theoretically-informed model of social connec- tions that takes account of their structural, functional and quality factorsandmadeuseofarepresentativecohortofolderadultsliving in England21. Our research questions were (i) are strong social con- nectionsrelatedtodeceleratedageing,eithersubjectiveorphysiolo- gical?(ii)howdopotentialeffectsdifferbasedonstructural,functional andqualityaspectsofsocialrelationships?and(ii)istheresynergyin the relationship between social connections and rates of subjective andphysiologicalageing?Hereweshowthatstructuralfactorssuchas livingaloneandlowsocialintegrationaswellasthefunctionalfactorof lowsocialsupportareriskfactorsforphysiologicalageacceleration. However, weak social connections are not related in a statistically significantwaytooldersubjectiveage.

Results
Descriptivestatistics
Weuseddatafromalarge-scalepanelstudyofadultsaged50andover livinginEngland;theEnglishLongitudinalStudyofAgeing(ELSA).Of the7047adultswithrelevantsocialandphysiologicalvariableseligible forinclusionintheanalyses,theaverageagewas65.6years(SD9.3) and55.2%werefemale.

We used a rich battery of questions on diverse aspects of structural, function and quality-related aspects of social connections. There were only weak to moderate correlations betweenmeasuresofsocialconnections,supportingtheirinclusion as separate items within the analyses (Fig. 2a and Supplementary Fig. 1), although people who had clear deficits in structural social factors like living alone scored lower on functional aspects like havinglowersocialsupportandhigherloneliness,andthequalityof socialrelationshipswasgenerallypooreramongstthosewithpoorer functionalaspectstoo.

Subjectiveagewasderivedbyaskingparticipantshowoldtheyfelt (measuredinyears)andsubtractingchronologicalagefromthis,with negative scores indicating older subjective age, and positive scores indicatingoldersubjectiveage.Forphysiologicalageacceleration,we usedapreviously-validatedmeasurecombiningclinicalindicatorsper- taining to the cardiovascular, respiratory, haematological and meta- bolic and musculoskeletal systems (Fig. 1). Negative scores indicate deceleratedageing(slowerphysiologicalageing),andpositivescores indicateacceleratedageing(fasterphysiologicalageing).Notably,there wasnostatisticallysignificantcorrelationbetweensubjectiveandphy- siological age (r=−0.01, p=0.56) (histogram and heatmap shown in Figs.2cand1d).Peoplealmostexclusivelyratedtheirsubjectiveageto bethesameorlowerthantheirchronologicalage(92%thesameor below),whilephysiologicalagewasmoreevenlydistributedaboveand belowchronologicalage,withaslightskewtowardspeoplebeingolder physiologicallythanchronologically(33%thesameorbelow,Fig.2b). There was a slight correlation between older chronological age and younger subjective age and a marked correlation between older chronologicalageandgreaterphysiologicalageacceleration(scatter- plotsinFig.2e,fandhexagonheatmapsinSupplementaryFig.2).

Mainanalyses
SamplecharacteristicsareshowninTable1.Forourmainanalyses,we calculatedaveragetreatmenteffects(ATEs)fromdoublyrobustesti- mationusingtheinverse-probability-weightedregressionadjustment (IPWRA) estimator. We adjusted for demographic, socio-economic, health and behavioural confounders within models, carrying out multiplesensitivityanalysestoconsiderrobustnesstovariousmodel assumptions.

Structure.Peoplewholivedaloneperceivedtheiragetobe1.4years youngerthanitwas(95%CI0.7to2.2yearsyounger),whichwas16% lowerthan peoplewholivewithothers(forfullresultsseeFig.3& Table2).Buttheirphysiologicalagewas2.6yearsolder(95%CI1.4to 3.8yearsolder;50%greaterageaccelerationthanpeoplewholivewith others).Peoplewhohadlowlevelsofsocialintegrationalsodidnot haveastatisticallysignificantoldersubjectiveage,althoughtheydid showpatternsofacceleratedphysiologicalage(1.9yearsolder,95%CI 0.8to3.0yearsolder,37%greaterageaccelerationthanthosewho weremoresociallyintegrated).

Amongstmoretentativefindings,peoplewhoweremostsocially isolatedperceivedthemselvesbe0.9yearsyoungerthantheirchron- ologicalage(95%CI0.2to1.7yearsyounger;10%greateragedecel- erationthanthosenotsociallyisolated;althoughthisdidnotsurvive correctionformultiplecomparisons),whereastheirphysiologicalage wasnotmateriallydifferentstatistically.Havingasmallsocialnetwork wasalsoassociatedwithbeing0.9yearsolder(95%CI0.04to1.8;17% greaterageacceleration;althoughthisdidnotsurvivecorrectionfor multiple comparisons), but there was no statistically significant dif- ferenceinsubjectiveage.

Function. People who had low levels of social support or high levelsoflonelinessshowednostatisticallysignificantdifferences intheirsubjectiveage.Butlowsocialsupportwasassociatedwith physiologicalageacceleration(average1.9yearsolder,95%CI0.9 to 2.8 years older, 35% greater age acceleration than those with- outlowsocial support).

Quality.Highsocialstraindidnothaveastatisticallysignificantasso- ciationwithsubjectiveageacceleration,butitdidshowsomemarginal associations with physiological age deceleration (1.2 years younger, 95%CI 0.2 to 2.1 years; although this did not survive correction for multiplecomparisons).

Sensitivityanalyses
Overall,resultswerelargelyconsistentacrosssensitivityanalyses,with someresults—suchassmallsocialnetwork—morestronglyassociated witholderphysiologicalagewhentakingaccountofdepressionand outliers (Supplementary Figs. 3–5). Psychological age deceleration resultswerestrongestforlivingaloneinadultsundertheageof65and forsocialisolationforadultsovertheageof65(SupplementaryFig.6). Notably,adultsundertheageof65didreportfeelingolderiftheyhad lowlevelsofsocialintegration.Forphysiologicalageacceleration,low socialsupportwastheclearestpredictorsinunder65s,whereasall resultsfromthemainanalyseswereevidentinover65s.

When considering whether strong social connections might be protective,livingwithothersvslivingaloneshowedthesamefindings asforsocialdeficitanalyses(justinreverse)giventhebinarynatureof the variable (Fig. 4). No other factors were statistically significantly associatedwithsubjectiveagedeceleration,buthighsocialintegration andhighsocialsupportbutwerebothassociatedwithphysiological age deceleration. Those with high social integration were physiologically1.6yearsyounger(95%CI0.5to2.7yearsyounger,27% greateragedecelerationthanthosewithouthighsocialintegration).

When using subjective age and physiological age acceleration mea- suresfromfouryearslater,livingaloneandlowsocialsupportresults for physiological ageing were maintained (Supplementary Fig. 7). When applying an alternative computation of proportional dis- crepancyinage,findingsweremateriallyunaffected(Supplementary Fig. 8). When using continuous variables for plausibly continuous confounders, the results were similarly materially unaffected (Sup- plementaryFig.9).

Methods
Dataset
Data were derived from the English Longitudinal Study of Ageing (ELSA).ELSAisanationally-representativepanelstudyofpeopleaged 50andoverandtheirpartners,livinginprivatehouseholdsinEngland. The original sample was drawn from participants from the Health SurveyinEngland(HSE)in1998,1999and200158.Surveyadministra- tionisdoneviacomputer-assistedpersonalinterviewing.Thisresearch complies with all relevant ethical regulations. ELSA received ethics approvalmostrecentlyfromtheSouthCentral–BerkshireResearch Ethics Committee (23/SC/0112). Written informed consent was obtainedateachinterview.Nofurtherethicsapprovalwasrequiredfor the present study. The first wave of data collection commenced in 2002/2003,andparticipantshavebeenfollowedbienniallysince.We usedwave2(2004/5),asthiswaveincludedarichbatteryofsocialand physiologicalvariablesofrelevance.

We restricted participants to core ELSA members who had returnedtheself-completionquestionnairewheremostofourmain variablesofinterestweremeasured(n=8354),whowereovertheage of50(n=8129),andwhohadfulldataonexposuresandconfounders (n=7047).Withinthissample,6621haddataonsubjectiveageing,and 4113hadfulldataonphysiologicalageing.Inmainanalyses,thefinal samplewasallowedtovarybetweenanalysesforsubjectiveandphy- siologicalagetomaximisepower,butsensitivityanalysesrestricted thesamplejusttoindividualswhohadbothsubjectiveandphysiolo- gicalageing(n=3878).

Exposures
Structural. Structural aspects of social connections included living alone (vs with others), intimate network size (how many children, family and friends respondents had close relationships with; count variable split into lowest quartile indicating most deficit vs other quartiles), social integration (number of organisations, clubs and societies respondents was a member of, their engagement with diverse volunteering activities, and their engagement with diverse culturalactivities;countvariablesplitintolowestquartileofintegra- tionvsotherquartiles),andsocialisolation(whetherrespondentssaw orspokeonthephonetofamily,friendsorchildrenlessthanoncea week,resultinginascaleforeachfrom0to6,withthescoresaveraged andsplitintomostisolatedquartilevsotherquartiles)59.

Functional. Functional aspects of social connections included lone- liness(measuredusingthethree-itemsubscalefromtherevisedUCLA lonelinessscale,resultinginascalefrom3to9splitintomostlonely quartilevsotherquartiles[22]),andsubjectivesocialsupport(whether participantsfeeltheycanrelyonothers,canopenuptothem,andare notletdownbythem,askedseparatelyforpartner,children,relatives and friends with each question scored from 1 to 4, with the scores averagedandsplitintolowestsocialsupportquartilevsotherquartiles).

Quality.Qualityaspectsofsocialconnectionsincludedsocialstrain (whetherparticipantsfeeltheyarecriticisedbyothers,notunderstood bythem,andtheygetontheirnerves,askedseparatelyforpartner, children,relativesandfriendswitheachquestionscoredfrom1to4 generatingascaleof12–48splitintohigheststrainquartilevsother quartiles)59.

Social connections measures are summarised in Fig. 4 and full detailsofquestionsareprovidedinSupplementaryTable1.Figure4 adapts the pyramidal structure previously proposed in theoretical work on social connections that positions structural factors as underpinning and being critical to the experience of functional or qualityaspectsofsocialconnections39.

Outcomes
Subjectiveagewasderivedbyaskingparticipantshowold theyfelt (measuredinyears)andsubtractingchronologicalagefromthis.Asa result, negative scores indicate older subjective age, and positive scoresindicateoldersubjectiveage.

Aphysiologicalageaccelerationmeasure(measuredinyears)was derived using the principal component analyses (PCA) method, an established method to measure physiological ageing46,60. Itwas cre- atedusingclinicalindicatorspertainingtothecardiovascularsystem (pulse pressure, systolic blood pressure, diastolic blood pressure, mean arterial pressure), respiratory system (forced vital capacity [FVC],forcedexpiratoryvolumeinonesecond[FEV]),thehaemato- logicsystem(haemoglobinconcentration,fibrinogen,C-reactivepro- tein [CRP], ferritin), metabolic system (fasting glucose, glycated haemoglobin,totalcholesterol,LDLcholesterol,HDLcholesterol,tri- glyceride),andmuscleandfat(gripstrength,waistcircumference).All measuresweretakenatwave2innursevisits(blood-basedbiomarkers andphysiologicaltesting).Thisindexfollowedtheprocesspreviously describedforcreatingaphysiologicalageingindex27.

The full details of physiological age derivation using the PCA method are summarised in Fig. 4 and Supplementary Materials (Appendix1;SupplementaryTable2andFigs.10and11).Tocreatethe physiological age acceleration/deceleration measure, chronological agewassubtractedfromphysiologicalage;anapproachextensively usedinpreviousresearch47,61–63.Asinpreviousstudies,thismeansthat acceleratedphysiologicalageingreferstoachronologicalagehigher thanaphysiologicalageratherthannecessarilyimplyalongitudinal change.Negativescoresindicatedeceleratedageing(slowerphysio- logicalageing),andpositivescoresindicateacceleratedageing(faster physiologicalageing).Analysesofthisindexshowitsassociationwith incidentfunctionalandmobilitylimitations,memoryimpairment,and diverse chronic conditions in the ELSA study population (Supple- mentaryTable3).

Confounders
Factorsidentifiedaspredictingbothreceptiveartsengagementand mortality were identified using directed acyclic graphs (DAGs) and included as covariates64. Demographic and socio-economic con- foundersincludedchronologicalage(inyears),sex(maleorfemale; ELSAsurveymaterialsrefertosexnotgender),ethnicity(whiteBritish vs other), educational qualifications (no educational qualifications; education to GCE/O-levels/national vocational qualification (NVQ) 2 (qualificationsatage16);educationtoNVQ3/GCE/A-levels(qualifica- tionsatage18);higherqualification/NVQ4/NVQ5/degree),totalnon- pensionwealth(whichcombinesnetfinancialandphysicalwealthplus net owner-occupied housing wealth; categorised in quintiles)65, and houseownership(whetherindividualsownedtheirpropertyoutright vswithamortgage/renting/socialhousing/other).

Health and behavioural factors were more complextotheorise withinmodelsastheycanbebothpredictorsofsocialconnectionsand mediators of causal relationships to ageing-related outcomes.How- ever,longitudinalresearchandtheresultsofclinicaltrialsgenerally showthatsocialrelationshipsarerelativelyweakpredictorsoffuture health behaviours, while health behaviours more strongly predict social connections66–68. So we included whether participants were sedentary (categorised as engaging in mild, moderate or vigorous activity less than once a week), frequency of alcohol consumption (morethan5daysaweekvsless),andwhetherparticipantscurrently smoked. We also acknowledge a bidirectional relationship between socialconnectionsanddisease21.However,giventhatchronicillnesses have marked effects on physical and psychological capacity to undertake social behaviours, we included whether participants reported currently having a diagnosis of any chronic conditions including cancer, lung disease or cardiovascular disease (including highbloodpressure,angina,apreviousheartattack,heartfailure,a heartmurmur,anabnormalrhythm,diabetes,apreviousstroke,high cholesterol, or other heart trouble), arthritis, asthma, osteoporosis, Parkinson’sdisease,Alzheimer’sdiseaseordementia.Butthisvariable was provided as a simple binary to acknowledge the presence of a capacity-influencing chronic illness while minimising the risk of accountingforpartialmediationoftheeffectoftheexposureonthe outcome.

Morecomplexpotentialconfoundersweredifficultiesincarrying outactivitiesofdailylivingthatparticipantsreported(ADLs;countof challengesincludingdressing,bathing,eating,usingatoilet,shopping, taking medications or making telephone calls), self-reported health (poor,fair, good, very good, excellent), BMI (<25, 25–30, ≥30), and depression(usingtheCentreforEpidemiologicalStudiesDepression (CES-D)scale,≥3).Forallofthese,literaturesuggeststrongbidirec- tionalrelationships,particularlyforBMIanddepressioninrelationto socialconnections,withcertainaspectsofsocialconnectionssuchas loneliness sharing highly overlapping genetic influences17,69. Addi- tionally,ADLsandself-reportedhealthcouldbearguedtooverlapwith componentsofouroutcomemeasures.Consequently,weconducted models with and without these factors to test the consistency of findings,asoutlinedbelow.

Statistics
Datawereanalysedusingdoublyrobustestimationusingtheinverse- probability-weighted regression adjustment(IPWRA)estimator. This involves building two models to account for the non-random treat- mentassignment:aregressionadjustmentmodelfortheoutcomeand atreatment-assignmentmodelfortheexposure,onlyoneofwhichhas tobecorrectlyspecified,enhancingtherobustnessoftheanalysis70. This approach thus provides two chances to make valid inferences, insteadofjustone,aslongaseitherthetreatmentgrouporoutcome canbedeterminedasafunctionofthecovariatesandthereareno unobserved confounders. IPWRA estimators apply weighted regres- sion coefficients to compute averages of treatment-level predicted outcomes,wheretheweightsaretheestimatedinverseprobabilitiesof treatment.FollowingourDAGs,confoundersappliedtobothexposure andoutcomeweresex,ethnicity,wealth,education,houseownership, sedentary behaviours, alcohol consumption, smoking, and chronic conditions.ProblemswithADLsandself-reportedhealthwereaddi- tionally applied to the exposure, and BMI and depression to the outcome.

We estimated the average treatment effect in the population (ATE),whichistheaveragedifferenceinoutcomeifeveryoneinthe population experienced the exposure, versus no-one inthe popula- tion. We also estimated the potential-outcome means (POMs) by regressionadjustment,whichusecontrastsofaveragesoftreatment- specificpredictedoutcomestoestimatetreatmenteffects,andused thistocalculatetheATEasapercentageoftheuntreatedPOM(pre- sentedwiththedelta-method-basedstandarderror)71.Separatemod- elswereestimatedforeachoftheeightexposures,soaBonferroni alpha of 0.05/8 or 0.006 accounts for multiple corrections. For all analyses, we applied probability weights for the self-completion questionnaireprovidedinthedata,whichaccountforcomplexsam- pling strategies and non-response. All analyses met model assump- tionsandalltestsconductedweretwo-tailed.Analyseswerecarried outinStatav18.

Depressioncouldplausiblylieonthecausalpathwayfromsocial connectionstoageing,soitwasonlyincludedintheoutcomecon- founder model, not the exposure confounder model, in the main analyses. However, a sensitivity analysis additionally accounted for depressionwithintheexposuremodel.Asecondsensitivityanalysis restrictedthesampletoonlypeoplewhohaddatabothforthesub- jective and physiological ageing indices. A third sensitivity analysis excluded anybody whose subjective or physiological age was more than 30yearsabove or below their chronological age to assessthe stability of the findings without the influence of outliers. A fourth sensitivityanalysesdichotomisedtheresultsbyage(splitat65years) inordertoascertainwhetherresultswereconsistentacrossageing.A fifth sensitivity analysis recategorized the quartiles such that the quartileshowingleastsignofsocialdeficitandmostsignofstrong socialconnectionswastheexposedpopulationtoascertainwhether the findings were the same for strong rather than weak social con- nections.Asixthsensitivityanalysisrepeatedthemainanalysesbut usingsubjectiveageandphysiologicalageaccelerationmeasuredfour yearslater,toassesswhetherfindingsheldlongitudinallyaswellas cross-sectionally. A seventh sensitivity analysis repeated the main analysisusinganalternativealgorithmtocalculateproportionaldis- crepancyinage:ageindex-chronologicalage/chronologicalage44.An eighthsensitivityanalysisalsorepeatedthemainanalysisbutremov- ingcognitivemeasuresformthephysiologicalageingindextoprovide standardisationwithpreviouspapers.Afinalsensitivityanalysiscon- sideredtheconsistencyofresultswhenusingcontinuousvariablesfor plausiblycontinuousconfounders.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript is well-written, with a strong and rigorous methodology that enhances the credibility of its findings. The\nauthors have effectively utilized robust analytical approaches to examine the association between social connections and\nphysiological aging. The study makes a valuable contribution to the field by highlighting the role of structural aspects of\nsocial connections in shaping biological aging processes. I have a few suggestions for a minor revision to further strengthen\nthe manuscript.\nMethods:\n• There are no references provided for structural aspects of social connection, as well as social support and strain. Please\ninclude relevant citations to support these constructs.\n• Please modify “gender (male and female)” to “sex” for accuracy in terminology.\nDiscussion:\nThis study provides novel insights by demonstrating that structural aspects of social connections—such as living alone or\nlacking social integration—are most strongly associated with physiological aging. However, frailty—a key age-related state\ncharacterized by increased vulnerability to stressors—may also play a crucial role in this mechanism, yet it was not explicitly\nconsidered in the conceptual framework. Given that both social isolation and frailty are stressful conditions that activate\ninflammatory pathways and contribute to health deterioration, frailty could serve as an important explanatory factor in the\nobserved associations. Since frailty is closely linked to systemic inflammation, immune dysregulation, and physiological\ndecline, it may help explain how social deficits accelerate biological aging.\nWhile the authors have controlled for multiple indicators of frailty (e.g., ADL, IADL, chronic conditions, and cognitive\nfunction), explicitly framing frailty as a key part of the theoretical model could enhance the interpretation of the findings.\nGiven its bidirectional relationship with social isolation—where socially isolated individuals are at higher risk of developing\nfrailty, while frailty itself can lead to further social withdrawal—it may serve as a key mechanism linking social deficits to\naccelerated biological aging. Addressing frailty within the discussion would strengthen the study’s contribution to\nunderstanding the complex interplay between social connections and physiological aging.\n(Remarks on code availability)\nThe supplementary files are very comprehensive; however, I did not see any code included. If the journal’s policy requires\nsharing code, it may be helpful to provide the code used for the Principal Component Analysis (PCA) to enhance\ntransparency and reproducibility.", "reviewer_2": "The paper examines the relationship between social deficits and ageing, using perceived and physiological age as outcome\nmeasures. While the study benefits from a large dataset and the inclusion of multiple biological and self-reported indicators,\nthere are substantial conceptual and methodological weaknesses that limit the validity of its conclusions. Key concerns\ninclude ambiguous causal claims, Inconsistent variable classification, methodological weaknesses in subjective age\ncalculation; questionable categorization of continuous variables; unclear mechanistic claims. Given these issues, the\ninterpretability and robustness of the findings remain questionable.\n• Abstract & throughout the ms: The manuscript states that the study provides insights into ageing-related mechanistic\nprocesses, yet the analysis primarily examines associations between perceived age, physiological age, and social support.\nThere is no clear justification for how these associations inform underlying mechanisms of ageing. Correlational findings do\nnot establish causal pathways or explain biological or psychological processes that drive ageing. Without a theoretical or\nempirical framework linking these variables to mechanistic ageing processes, the claim remains unsupported. The authors\nshould either (1) clearly articulate how their findings contribute to mechanistic understanding or (2) revise their conclusions\nto reflect that the study identifies associations rather than mechanisms.\n• Abstract & throughout the ms: The term \"social deficits\" to describe lack of social support, loneliness, and related\nconstructs is problematic, as it implies a deficiency-based or pathological framing of social experiences. These are context-\ndependent and multidimensional and should not be reduced to a deficit model without justification. The authors should use\nmore precise terms, such as \" social support,\" \"social isolation,\" or \" social networks,\" to align with established terminology.\n• The manuscript contains several typographical errors, such as \"metaboloic\" (Abstract). A thorough proofreading is\nnecessary to ensure accuracy and clarity throughout the text.\nINTRODUCTION\n• The claim that \"social deficits increase with age\" oversimplifies social changes in later life. Research, including\nSocioemotional Selectivity Theory (SST), shows that older adults prioritize quality over quantity in relationships rather than\nexperiencing a uniform decline. Reduced social contact does not necessarily indicate a deficit. The authors should consult\nexisting theories on social support across the lifespan and revise this statement to reflect these nuanced patterns.\n• The introduction is lengthy in parts, particularly in its deep dive into the history of biological ageing, yet it lacks a clear\ntrajectory toward the study’s main research question as there is no link to established physiological. As a result, the reader is\nleft uncertain about the study’s focus and contribution. The authors should streamline the intro, ensuring that each section\ndirectly builds toward the study’s aims.\n• Again, one of the biggest issues is the misuse of causal language in an observational study, such as stating that \"biological\nage has been strongly influenced by social deficits.\" This implies a directional effect that has not been empirically\nestablished. The authors should use association-based language (e.g., \"is correlated with\") unless they provide strong\ntheoretical and methodological evidence to support causal claims. Ensuring precise language is essential for clarity and\nvalidity.\n• The term \"perceived age\" is too ambiguous, as it is often used to describe how others perceive a person’s age, rather than\nan individual's own subjective experience of ageing. If the authors are referring to how old participants feel rather than how\nold they appear to others, the more precise term would be \"subjective age\" or “felt age”. Clarifying this terminology would\nprevent misinterpretation and align with established literature on self-perceptions of ageing.\n• Referring to perceived age as a biomarker is conceptually problematic. While perceived age can be an important predictor\nof health outcomes and mortality, it does not meet the standard definition of a biomarker, which refers to objective, biological\nmeasures. Perceived age is a subjective or socially influenced measure rather than a direct biological marker of ageing. The\nauthors should reconsider this classification.\n• The claim that \"perceived age provides a mental map of one’s remaining life expectancy\" is overstated. While subjective\nage correlates with subjective life expectancy, they are distinct constructs and do not perfectly overlap. The authors should\nrevise this statement to reflect their partial but imperfect association.\nMETHODOLOGY\n• A major methodological issue in the paper is the categorization of continuous variables into extreme groups (e.g., lowest\nvs. quartiles). Dichotomizing or grouping continuous measures, such as depression (yes, no); activity (yes no), etc. , leads to\na significant loss of information, reduces statistical power, and may introduce artificial thresholds that do not reflect the\nunderlying distribution of the data. If the variable is inherently continuous, the authors should avoid arbitrary splits and\ninstead model it as a continuous predictor to retain the full variability and improve analytical precision.\n• The term \"perceived age acceleration\" is misleading and conceptually incorrect in this context. The data only capture a\nsingle time point, meaning it is impossible to determine whether participants have experienced a change/acceleration in the\ndiscrepancy between subjective age and chronological age over time. Acceleration implies an increasing discrepancy over\nmultiple time points, which is how the term is used in the existing literature. If the difference between subjective and\nchronological age is stable, it does not reflect acceleration. It’s possible that some people have always felt this way and this\nis a trait with zero acceleration. The authors should either revise the terminology to avoid implying change over time or\nincorporate longitudinal data if acceleration is the intended construct.\n• Same is true for the term \"physiological age acceleration\". It is misleading and conceptually incorrect as the data only\ncapture a single time point, meaning it is impossible to determine whether participants have experienced a\nchange/acceleration in the discrepancy between physiological age and chronological age.\n• The calculation of subjective/felt age as \"perceived age minus chronological age\" does not appropriately account for age-\nrelated scaling differences. A one-year discrepancy is more meaningful for a 20-year-old than for a 90-year-old. To address\nthis, most studies compute a proportional discrepancy by dividing the subjective age difference by chronological age (i.e.,\n(Subjective Age - Chronological Age) / Chronological Age), which better accounts for relative differences across the\nlifespan. The authors should consider aligning their approach with this established method (Rubin and Berntsen 2006;\nStephan et al.2015) to improve comparability with existing literature. Also, proportional discrepancy scores three standard\ndeviations above or below the mean need to be considered outliers (Stephan et al. 2015; Weiss and Lang 2012) and\nreplaced.\n• The approach used to derive the physiological age acceleration measure raises several methodological concerns:\no Heterogeneous Temporal Dynamics: The included physiological systems (cardiovascular, metabolic, cognitive – is this\neven physiological?) , etc.) exhibit different rates of change across the lifespan. Some measures (e.g., blood pressure, grip\nstrength) might show a more linear age-related decline, while others (e.g., cognitive function) might exhibit nonlinear\ntrajectories and are influenced by cumulative exposures. Combining these without considering their distinct temporal\norders may lead to severe misinterpretation of ageing processes.\no Causality and Interdependencies: The included indicators are not independent—some serve as causes, others as\nconsequences of physiological ageing. For example, metabolic dysfunction may contribute to cardiovascular deterioration,\nwhich in turn affects cognition. Ignoring these interdependencies may distort the meaning of the derived physiological age\nacceleration scores.\no Principal Component Analysis (PCA) seems inappropriate → PCA assumes that the included variables represent\nindependent dimensions of variation, but ageing is a highly correlated, hierarchical process. Using PCA for dimensionality\nreduction may mix cause and consequence, and the first component may capture variance unrelated to ageing per se.\nAlternative methods such as structural equation modeling (e.g., structural equation modeling) could better account for\nhierarchical dependencies.\no The authors include chronological age in the PCA, predict component scores, examine correlations, and then remove\nchronological age in a second PCA to compute physiological age. This approach introduces two key issues. First, including\nchronological age in the first PCA forces the first component to capture chronological age variance, making it unclear\nwhether the component represents ageing-related physiological changes or just chronological age itself. Second, By\nrunning PCA again without chronological age, the method disconnects the derived score from the variable initially used to\ndefine biomarker importance. This creates an inconsistency in how ageing is defined and how the final score is constructed.\no The calculation of physiological age acceleration (physiological age – chronological age) assumes a linear relationship\nbetween physiological and chronological ageing. However, this assumption is problematic given the nonlinear nature of\nbiological ageing. Some physiological systems may decline early (e.g., lung function), while others remain stable longer\n(e.g., cognitive function), making a single age difference score potentially misleading.\no While the authors state that the index is associated with functional limitations and chronic conditions, they should provide\nrigorous validation analyses, including:Comparison with existing biological age models (e.g., epigenetic clocks, telomere\nlength, brain age, skin age, etc. etc.).\no Biomarkers were pre-selected based on their correlation with chronological age (r ≥ 0.10), ensuring that the final\nphysiological age score is artificially correlated with age. This approach excludes biomarkers that may reflect physiological\nageing independent of chronological age, potentially skewing results. It is possible that an indicator is not highly correlated\nat one point in the lifespan but yet predictive for mortality outcomes.\no The chosen correlation cutoffs (r ≥ 0.10 for selection, etc.) appear arbitrary and lack justification. A weak correlation with\nage (r ≥ 0.10) does not ensure that a biomarker reliably captures physiological ageing. A moderate correlation (r ≥ 0.30) with\nthe PCA score does not confirm biological relevance, as PCA prioritizes variance, not ageing-specific processes.\no Most importantly, PCA assumes that all retained physiological measure the same underlying construct, ignoring the\nheterogeneous and multi-system nature of ageing. Ageing affects different systems (e.g., metabolic, immune, cardiovascular)\nat different rates; reducing it to a single component oversimplifies complex biological processes and ignoring the\nheterogeneous nature of ageing across different organ systems.\n• The use of doubly robust estimation with the IPWRA estimator in a cross-sectional study raises some methodological\nconcerns. First, the lack of temporal ordering prevents causal inference, as exposure and outcome are measured\nsimultaneously. Second, unobserved confounders may bias the results. Third, inverse probability weights (IPWs) are more\nappropriate for longitudinal studies, where they adjust for selection bias over time; their use in a cross-sectional setting is\nless effective. Fourth, many covariates, including ses and health behaviors, vary over time, but the analysis relies on a\nsingle time-point measurement. Finally, the cross-sectional design limits the ability to conduct sensitivity analyses to\nassess time-dependent confounding or feedback effects.\n• The classification of variables as confounders, mediators, or outcomes appears arbitrary and lacks clear conceptual and\nempirical justification. For example, depression could plausibly act as a mediator, confounder, or bidirectional factor in the\nrelationship between social connections and ageing, depending on the theoretical framework. The decision to include\ncertain variables in the exposure vs. outcome confounder models seems inconsistent and requires a stronger theoretical\nrationale.\n• Table 1 lists \"No. of chronic conditions\" as \"Yes/No,\" which seems wrong—a count variable cannot be binary. The authors\nshould clarify whether they measured the number of conditions or the presence of any condition and adjust the\ncategorization accordingly.\n• The manuscript lacks a full intercorrelation table for all study variables, except for social support. Given the potential for\nmulticollinearity, particularly among ageing-related psychological measures, the authors should provide a full correlation\nmatrix to assess the extent of redundancy and ensure the validity of their models.\n• The pyramid structure used for the social connection model implies an implicit hierarchy or ordering of social relationships\nthat lacks empirical justification. If the model suggests qualitative differences between levels, the authors should provide\nempirical support for this structure. Otherwise, an alternative visualization that does not imply a strict ranking may be more\nappropriate.\n• Resolution of figures is poor.\nDISCUSSION:\n• Additionally, the literature review is highly selective, overlooking key studies in ageing, social support, and biological\nageing. The authors should engage more comprehensively with existing research to provide a balanced and accurate\nfoundation for their work.\n• The claim that the study provides \"novel insight into a plausible mechanism\" linking social deficits to ageing is overstated\nand inaccurate. Demonstrating an association between social support and a composite ageing index does not establish a\nmechanism, as mechanisms require causal pathways explaining how social factors biologically influence ageing. The\nauthors should clarify their contribution—if they are identifying correlations, they should not claim mechanistic insight.\nAdditionally, the novelty of this finding is questionable, given prior research on social determinants of(subjective and\nbiological) ageing.\n• Similarly, the claim that these findings \"provide insight into how social deficits influence age-related pathology\" is\noverstated. Additionally, the statement that \"objective biological and functional processes may be more important\nmechanistically than broader processes related to subjective perceptions\" is unclear and lacks empirical justification. The\nauthors should clarify their reasoning, define what is meant by \"broader processes,\" and avoid mechanistic claims.\n(Remarks on code availability)\nDid not see the code.", "reviewer_3": "Authors used ELSA data to evaluate social connections (structural, functional, and quality) on perceived and physiological\naging. Overall, the study was well written and contributes to the literature in a meaningful way. However, I have a few\nrecommendations to strengthen the contribution and justification for the design of the study.\nIntroduction:\nI would recommend rewording the lines 44-54 as it makes it seem that physiological age is new – even though it precedes\nthe development of epigenetic clocks and metabolic clocks. I would perhaps also focus on the fact these types of clocks\nwere not trained on prediction of outcomes but rather incorporate physiological aging processes themselves (unlike\nepigenetic clocks that were trained on outcomes).\nI am uncertain about the current motivation for perceived age and physiological age. It seems like two different papers to me.\nI would recommend some additional incorporation that may answer is one related to health better than the other? How are\nthey related to each other in other studies since it has low correlation here? Why are both necessary to understand the\nmechanistic processes? Giving further motivation here would help to strengthen the contribution of the paper and highlight\nwhy we need to consider perceived (even if you the authors do not find as robust findings).\nMethods:\nCan the authors motivate the addition of cognitive functioning as a component of physiological age, as cognition is not\npurely a physiological process but a combination of cognitive development and pathology. To justify this use, I might also\nrecommend the authors do a sensitive check without cognitive functioning to keep in line with prior iterations. I do recognize\nthat it is unlikely to change the results as cognitive functioning often runs parallel to the other components, but conceptual\nclarity would be helpful here.\nDiscussion:\nWhat does the role of mortality selection have on these models? Is this a limitation to consider? I appreciate the 4 years\nfollow up but I am still wondering about sample selection and survival and important processes to consider.\nCould the authors also add that people with health conditions may also have more complications with social participation,\nwhich itself may lead to faster aging? I realize that the living alone and living with someone may partly address this\nconcern (especially with severe health conditions) but feel it may be an important note to consider for future work (or\neven a potential sensitivity check).\nMinor comments:\nYou write the first aging clocks in the introduction. I would reword to state the first-generation epigenetic aging clocks were\ntrained on chronological age. While not called aging clocks per se, other algorithms like PhenoAge served a similar purpose\nand may confuse readers.\nPlease specify if sample was community dwelling adults. If so, that may also be a limitation to consider. But I am uncertain of\nthe commonality of nursing homes in the UK context.\n(Remarks on code availability)"}, "author_response": "Response to reviewers\nReviewer #1 (Remarks to the Author):\nThe manuscript is well-written, with a strong and rigorous methodology that enhances the credibility of its findings. The\nauthors have effectively utilized robust analytical approaches to examine the association between social connections and\nphysiological aging. The study makes a valuable contribution to the field by highlighting the role of structural aspects of\nsocial connections in shaping biological aging processes. I have a few suggestions for a minor revision to further\nstrengthen the manuscript.\nWe are grateful for the positive feedback from the reviewer.\nMethods:\n• There are no references provided for structural aspects of social connection, as well as social support and strain. Please\ninclude relevant citations to support these constructs.\nWe have now provided more citations for these measures.\n• Please modify “gender (male and female)” to “sex” for accuracy in terminology.\nThis has now been done\nDiscussion:\nThis study provides novel insights by demonstrating that structural aspects of social connections—such as living alone or\nlacking social integration—are most strongly associated with physiological aging. However, frailty—a key age-related state\ncharacterized by increased vulnerability to stressors—may also play a crucial role in this mechanism, yet it was not\nexplicitly considered in the conceptual framework. Given that both social isolation and frailty are stressful conditions that\nactivate inflammatory pathways and contribute to health deterioration, frailty could serve as an important explanatory\nfactor in the observed associations. Since frailty is closely linked to systemic inflammation, immune dysregulation, and\nphysiological decline, it may help explain how social deficits accelerate biological aging. While the authors have controlled\nfor multiple indicators of frailty (e.g., ADL, IADL, chronic conditions, and cognitive function), explicitly framing frailty as a\nkey part of the theoretical model could enhance the interpretation of the findings. Given its bidirectional relationship with\nsocial isolation—where socially isolated individuals are at higher risk of developing frailty, while frailty itself can lead to\nfurther social withdrawal—it may serve as a key mechanism linking social deficits to accelerated biological aging.\nAddressing frailty within the discussion would strengthen the study’s contribution to understanding the complex interplay\nbetween social connections and physiological aging.\nThank you for this point. In response to comments from the other reviewers, we have now added a new paragraph on the\ndirectionality of our effects in the discussion and we explicitly mention frailty as a potential explanatory factor through\nwhich accumulation of biological ageing and loss of reserves leads to changes in social behaviours (discussion paragraph\n2).\nReviewer #1 (Remarks on code availability):\nThe supplementary files are very comprehensive; however, I did not see any code included. If the journal’s policy requires\nsharing code, it may be helpful to provide the code used for the Principal Component Analysis (PCA) to enhance\ntransparency and reproducibility.\nThe code is all available publicly via OSF. https://github.com/dfancourt/socialdeficits\nReviewer #2 (Remarks to the Author):\nThe paper examines the relationship between social deficits and ageing, using perceived and physiological age as outcome\nmeasures. While the study benefits from a large dataset and the inclusion of multiple biological and self-reported\nindicators, there are substantial conceptual and methodological weaknesses that limit the validity of its conclusions. Key\nconcerns include ambiguous causal claims, Inconsistent variable classification, methodological weaknesses in subjective\nage calculation; questionable categorization of continuous variables; unclear mechanistic claims. Given these issues, the\ninterpretability and robustness of the findings remain questionable.\nWe are grateful to the reviewer for their comments on this manuscript and they raise some important points. We feel it is\nimportant to clarify that our aim was not produce an externally-validated or central physiological age, but to produce a\nuseful summary measure of physiological ageing in the ELSA cohort. The method we use has already been published and\nvalidated elsewhere, as we reference, and we are able to demonstrate that many of the issues the reviewer raises below\nare actually not problems in our index (e.g. linearity and correlations with chronological age). We have also provided much\nmore information within the limitations section of the paper and undertaken multiple new analyses in line with the\nreviewer’s recommendations. We acknowledge that there are many different ways to derive ageing indices, but we still\nbelieve that our method has value. We have articulated this value and why these analyses remain important in our paper\nand the responses below.\n• Abstract & throughout the ms: The manuscript states that the study provides insights into ageing-related mechanistic\nprocesses, yet the analysis primarily examines associations between perceived age, physiological age, and social support.\nThere is no clear justification for how these associations inform underlying mechanisms of ageing. Correlational findings\ndo not establish causal pathways or explain biological or psychological processes that drive ageing. Without a theoretical\nor empirical framework linking these variables to mechanistic ageing processes, the claim remains unsupported. The\nauthors should either (1) clearly articulate how their findings contribute to mechanistic understanding or (2) revise their\nconclusions to reflect that the study identifies associations rather than mechanisms.\nWe have now removed all assertions that we have identified new mechanisms. Instead, we have focused the paper much\nmore clearly on demonstrating that there is a relationship between social deficits and the combined index of these\nphenotypic markers that considers their predictive potential for ageing. Given this, we have proposed a hypothesis that\naccelerated physiological age is a potential causal mechanism by which social deficits could influence age-related\nmorbidity and mortality. While this study (which focused only on cross-sectional and some initial longitudinal data over\nfour years of follow-up) cannot make causal claims nor confirm such a mechanism, we put forward the hypothesis here as\nthe basis for future research.\n• Abstract & throughout the ms: The term \"social deficits\" to describe lack of social support, loneliness, and related\nconstructs is problematic, as it implies a deficiency-based or pathological framing of social experiences. These are context-\ndependent and multidimensional and should not be reduced to a deficit model without justification. The authors should\nuse more precise terms, such as \" social support,\" \"social isolation,\" or \" social networks,\" to align with established\nterminology.\nAcross the paper, each term is used individually in relation to our outcomes. We do not create any “index” that represents\nan overarching concept of “social deficits”. Instead, we use the term as to refer to a lack of social support, social isolation,\nlimited social networks etc. This is in line with the most recent theoretical thinking in the field e.g. Holt-Lunstad 2022\nhttps://doi.org/10.1146/annurev-publhealth-052020-110732. Each time we refer to specific types of social deficits we use\ntheir correct terminology, e.g. “social support”. However, we acknowledge that the term “deficits” can be problematic in\nsuggesting normative levels. So we have now rephrased this throughout as “weak social connections”.\n• The manuscript contains several typographical errors, such as \"metaboloic\" (Abstract). A thorough proofreading is\nnecessary to ensure accuracy and clarity throughout the text.\nThis has now been done, thank you.\nIntroduction\n• The claim that \"social deficits increase with age\" oversimplifies social changes in later life. Research, including\nSocioemotional Selectivity Theory (SST), shows that older adults prioritize quality over quantity in relationships rather than\nexperiencing a uniform decline. Reduced social contact does not necessarily indicate a deficit. The authors should consult\nexisting theories on social support across the lifespan and revise this statement to reflect these nuanced patterns.\nWe are grateful for this point, which we agree is important. We now clarify in the introduction: “Multi-national research\ndemonstrates that social connections change over the life-course, with individuals prioritising certain types of social goals\nat different life points, such as focusing more on quality rather than quantity of social connections as they age. However,\nthere are certain patterns that are well-evidenced and recognised as indicative not just of adaptive of compensatory\npatterns but instead of weaknesses in social connections that can be detrimental to health.” As described above, we have\nchanged our language from deficits to weak connections.\n• The introduction is lengthy in parts, particularly in its deep dive into the history of biological ageing, yet it lacks a clear\ntrajectory toward the study’s main research question as there is no link to established physiological. As a result, the reader\nleaves uncertain about the study’s focus and contribution. The authors should streamline the intro, ensuring that each\nsection directly builds toward the study’s aims.\nWe have now rewritten the introduction so that it is much more concise and provides a clear flow for our arguments.\n• Again, one of the biggest issues is the misuse of causal language in an observational study, such as stating that\n\"biological age has been strongly influenced by social deficits.\" This implies a directional effect that has not been\nempirically established. The authors should use association-based language (e.g., \"is correlated with\") unless they provide\nstrong theoretical and methodological evidence to support causal claims. Ensuring precise language is essential for clarity\nand validity.\nWe have now removed all statements that we can identify that appear too strongly causal.\n• The term \"perceived age\" is too ambiguous, as it is often used to describe how others perceive a person’s age, rather\nthan an individual's own subjective experience of ageing. If the authors are referring to how old participants feel rather\nthan how old they appear to others, the more precise term would be \"subjective age\" or “felt age”. Clarifying this\nterminology would prevent misinterpretation and align with established literature on self-perceptions of ageing.\nWe have now made this change throughout.\n• Referring to perceived age as a biomarker is conceptually problematic. While perceived age can be an important\npredictor of health outcomes and mortality, it does not meet the standard definition of a biomarker, which refers to\nobjective, biological measures. Perceived age is a subjective or socially influenced measure rather than a direct biological\nmarker of ageing. The authors should reconsider this classification.\nThis was actually a typo. We have now changed this term to “marker” instead.\n• The claim that \"perceived age provides a mental map of one’s remaining life expectancy\" is overstated. While subjective\nage correlates with subjective life expectancy, they are distinct constructs and do not perfectly overlap. The authors\nshould revise this statement to reflect their partial but imperfect association.\nWe have now removed this statement as part of our re-writing of the introduction.\nMETHODOLOGY\n• A major methodological issue in the paper is the categorization of continuous variables into extreme groups (e.g., lowest\nvs. quartiles). Dichotomizing or grouping continuous measures, such as depression (yes, no); activity (yes no), etc. , leads\nto a significant loss of information, reduces statistical power, and may introduce artificial thresholds that do not reflect the\nunderlying distribution of the data. If the variable is inherently continuous, the authors should avoid arbitrary splits and\ninstead model it as a continuous predictor to retain the full variability and improve analytical precision.\nWe have now re-run the main analyses using linear variables for all variables where this is possible, which shows no\nmaterial change in findings (see Supplementary Figure 12). We discuss the findings in the paper. This approach is not\npossible for the exposure, as we use a doubly-robust estimation approach, which is a statistical method for estimating\ncausal effects in different treatment groups. As such, it involves treatment assignment and does not work with linear\nvariables. Using this approach is important as it enables a more rigorous approach to confounders to be taken above\nlinear regression models, and it also responds to calls within social epidemiology as part of work on the potential\noutcomes framework and target trial emulation for clearer specification of target populations for public health\nintervention (which we now describe and cite in the paper). Naturally different cut-offs could be applied, but there are not\nwell-recognised standards or cut-offs in most social connections measures. So, a quartile approach provides a good\ncompromise between isolating individuals who are at the lower end of the scale in their scores but also maintaining\nsufficient statistical power to identify differences. Please note we already provide sensitivity analyses looking at different\nthresholds. We argue that the new analyses using linear covariates do enhance the paper and we have now provided\nmuch more discussion justifying the binary exposure while also considering the limitations within the methods and\ndiscussion.\n• The term \"perceived age acceleration\" is misleading and conceptually incorrect in this context. The data only capture a\nsingle time point, meaning it is impossible to determine whether participants have experienced a change/acceleration in\nthe discrepancy between subjective age and chronological age over time. Acceleration implies an increasing discrepancy\nover multiple time points, which is how the term is used in the existing literature. If the difference between subjective and\nchronological age is stable, it does not reflect acceleration. It’s possible that some people have always felt this way and this\nis a trait with zero acceleration. The authors should either revise the terminology to avoid implying change over time\nor incorporate longitudinal data if acceleration is the intended construct. Same is true for the term \"physiological age\nacceleration\". It is misleading and conceptually incorrect as the data only capture a single time point, meaning it is\nimpossible to determine whether participants have experienced a change/acceleration in the discrepancy between\nphysiological age and chronological age.\n“Accelerated physiological ageing” in the biological ageing literature refers to a chronological age higher than a biological\nage and in this context does not necessarily imply a longitudinal change and is a frequently-used term (e.g.\nhttps://doi.org/10.1186/s12889-024-17960-w, https://doi.org/10.1111/jgs.18611, https://doi.org/10.1186/s12889-025-\n22053-3.) We have now added additional text into the methods section with these references as justification for using this\nlanguage but caveating the fact that the term “accelerated physiological ageing” does not necessarily imply a longitudinal\nchange.\nHowever, we acknowledge that subjective age may be more accurately described as “older subjective age”, so we have\nmade this change throughout the manuscript.\n• The calculation of subjective/felt age as “perceived age minus chronological age” does not appropriately account for\nage-related scaling differences. A one-year discrepancy is more meaningful for a 20-year-old than for a 90-year-old. To\naddress this, most studies compute a proportional discrepancy by dividing the subjective age difference by chronological\nage (i.e., (Subjective Age – Chronological Age) / Chronological Age), which better accounts for relative differences across\nthe lifespan. The authors should consider aligning their approach with this established method (Rubin and Berntsen 2006;\nStephan et al.2015) to improve comparability with existing literature. Also, proportional discrepancy scores three standard\ndeviations above or below the mean need to be considered outliers (Stephan et al. 2015; Weiss and Lang 2012) and\nreplaced.\nThis is an important point if looking at the whole life course. But our sample only looks at adults over the age of 50, where\nthe relative difference in a year is less strong. Nonetheless, we appreciate the suggestion of an alternative approach. We\nhave now added a sensitivity analysis that uses the alternative approach suggested by the reviewer. (There were no\noutliers beyond 3 SD of the mean.) Notably, the results are materially unaffected, showing exactly the same patterns as in\nour original analyses (Supplementary Figure 10). So we have elected to keep our original analyses as the main analyses\ngiven they are more commonly used in analyses with older adults but we discuss the similarity of the findings using the\nalternative computation within the paper to highlight that our findings are not just an artefact of our chosen algorithm.\n• The approach used to derive the physiological age acceleration measure raises several methodological concerns:\no Heterogeneous Temporal Dynamics: The included physiological systems (cardiovascular, metabolic, cognitive – is this\neven physiological?) , etc.) exhibit different rates of change across the lifespan. Some measures (e.g., blood pressure, grip\nstrength) might show a more linear age-related decline, while others (e.g., cognitive function) might exhibit nonlinear\ntrajectories and are influenced by cumulative exposures. Combining these without considering their distinct temporal\norders may lead to severe misinterpretation of ageing processes.\nAs part of biomarker selection, we visually investigated the linearity of associations between physiological measures and\nchronological ageing using local polynomial regression. We have now added these plots to the Supplementary materials.\nThis line fitting technique is highly prone to overfitting the data, and therefore some deviation from linearity is to be\nexpected. Nonetheless, the trends we observed for the age range under consideration were overwhelmingly linear for all\nthe measures included in physiological age. As such, non-linearity of associations between biomarkers and physiological\nage is unlikely to pose an issue in this particular study population. We have now included a new figure (Supplementary\nFigure 1) plotting standardised biomarkers against chronological age using polynomial regression to illustrate this.\no Causality and Interdependencies: The included indicators are not independent—some serve as causes, others as\nconsequences of physiological ageing. For example, metabolic dysfunction may contribute to cardiovascular deterioration,\nwhich in turn affects cognition. Ignoring these interdependencies may distort the meaning of the derived physiological age\nacceleration scores.\nThere is no supposition in the derivation of physiological or biological ages that the indicators should be independent.\nIndeed, this is not possible or logical if the underlying theory is that ageing is driven by a set of central processes giving\nrise to the biomarker changes that we use to produce physiological age. Each indicator will serve as both a consequence\nof ageing processes, and a cause of subsequent biomarker changes, and it is not possible to distinguish between these or\nselect a set of biomarkers that are strictly consequences but not causes of other indicator changes. So our analysis makes\nno such supposition.\no Principal Component Analysis (PCA) seems inappropriate → PCA assumes that the included variables represent\nindependent dimensions of variation, but ageing is a highly correlated, hierarchical process. Using PCA for dimensionality\nreduction may mix cause and consequence, and the first component may capture variance unrelated to ageing per se.\nAlternative methods such as latent variable 5odelling (e.g., structural equation 5odelling) could better account for\nhierarchical dependencies. The authors include chronological age in the PCA, predict component scores, examine\ncorrelations, and then remove chronological age in a second PCA to compute physiological age. This approach introduces\ntwo key issues. First, including chronological age in the first PCA forces the first component to capture chronological age\nvariance, making it unclear whether the component represents ageing-related physiological changes or just chronological\nage itself. Second, By running PCA again without chronological age, the method disconnects the derived score from the\nvariable initially used to define biomarker importance. This creates an inconsistency in how ageing is defined and how the\nfinal score is constructed.\nThe PCA method Is among the most commonly used methods of biological age derivation (e.g.,\nhttps://pubmed.ncbi.nlm.nih.gov/12634284/, https://pubmed.ncbi.nlm.nih.gov/19940465/,\nhttps://pubmed.ncbi.nlm.nih.gov/19940465/, https://pubmed.ncbi.nlm.nih.gov/22433233/,\nhttps://pubmed.ncbi.nlm.nih.gov/24522464/, https://pubmed.ncbi.nlm.nih.gov/24659482/ ,\nhttps://pubmed.ncbi.nlm.nih.gov/3226152/, https://pubmed.ncbi.nlm.nih.gov/18597867/,\nhttps://pubmed.ncbi.nlm.nih.gov/18840798/, https://pubmed.ncbi.nlm.nih.gov/8803500/,\nhttps://doi.org/10.1186/s12889-024-17960-w). It is also amongst the most intuitive.\nWe agree with the reviewer (and we acknowledge in the paper) that there are limitations to the PCA method, though we\nonce again emphasise that our aim was not produce an externally-validated or central physiological age, but to produce a\nuseful summary measure of physiological ageing in the ELSA cohort.\nThough we agree with the reviewer that SEM methods are useful for more nuanced selection of indicators, in the present\nstudy, our interest is primarily in developing an easily interpretable summary measure of physiological age that can be\ncompared with chronological age. While we acknowledge that we lose some nuance in this approach, it is important to\nnote that SEM methods produce estimates that are not straightforward to compare with chronological age\n(http://dx.doi.org/10.1093/pnasnexus/pgac135, https://www.tandfonline.com/doi/full/10.2147/CIA.S134921#d1e2162).\nSo, for our study aims, the PCA method is more practical and meets our needs. We nonetheless agree with the reviewer\nthat the limitations of the method need to be better acknowledged in the limitations section, and have revised the\nmanuscript to reflect this, with an expanded discussion on this point in the limitations section.\no The calculation of physiological age acceleration (physiological age – chronological age) assumes a linear relationship\nbetween physiological and chronological ageing. However, this assumption is problematic given the nonlinear nature of\nbiological ageing. Some physiological systems may decline early (e.g., lung function), while others remain stable longer\n(e.g., cognitive function), making a single age difference score potentially misleading.\nPhysiological age is linearly associated with chronological age for the age range under study, and the indicators included\nare linearly related to age, so this is not a consideration for our study. We have now added a plot the Supplementary\nmaterials (Supplementary Figure 2) fitted using local polynomial regression that shows the linearity of the relationship.\no While the authors state that the index is associated with functional limitations and chronic conditions, they should\nprovide rigorous validation analyses, including comparison with existing biological age models (e.g., epigenetic clocks,\ntelomere length, brain age, skin age, etc. etc.).\nOn a biological level we disagree with this suggestion. It is not typical to see validations of physiological ageing indices\nagainst biological age models like epigenetic clocks as they measure different aspects of ageing (downstream phenotypic\nmeasures as incorporated in our index vs upstream telomere or epigenetic processes within the ‘omics cascade). It is\nwidely recognised that downstream vs upstream biological measures do not typically correlate as they represent very\ndifferent biological processes (e.g. https://elifesciences.org/articles/59479.pdf). To clarify, our physiological ageing index is\nnot meant to be comparable to telomere data or epigenetic clocks; it is meant to be a summary measure of phenotypic\naspects of physiological ageing in this population. (As an aside, methylation-based measures are not even included in\nELSA). Instead, physiological ageing measures based on clinical indicators are useful in settings where these data are not\navailable and do have a strong precedent, as outlined in the citations already provided above.\no Biomarkers were pre-selected based on their correlation with chronological age (r ≥ 0.10), ensuring that the final\nphysiological age score is artificially correlated with age. This approach excludes biomarkers that may reflect physiological\nageing independent of chronological age, potentially skewing results. It is possible that an indicator is not highly correlated\nat one point in the lifespan but yet predictive for mortality outcomes. The chosen correlation cutoffs (r ≥ 0.10 for\nselection, etc.) appear arbitrary and lack justification. A weak correlation with age (r ≥ 0.10) does not ensure that a\nbiomarker reliably captures physiological ageing. A moderate correlation (r ≥ 0.30) with the PCA score does not confirm\nbiological relevance, as PCA prioritizes variance, not ageing-specific processes.\nWe have chosen a summary measure of physiological age appropriate for our study question and have followed the PCA\nprotocol applied in other studies. Other methods of biomarker selection and biological age derivation such as SEM might\nbetter account for complex interrelationships between biomarkers. Nevertheless, our aim was to produce an easily\ninterpretable summary measure of physiological ageing that is straightforward to compare with chronological age, leading\nus to use the well-established PCA method. We now add new text acknowledging the limitations of the method within in\nthe limitations section.\no Most importantly, PCA assumes that all retained physiological measure the same underlying construct, ignoring the\nheterogeneous and multi-system nature of ageing. Ageing affects different systems (e.g., metabolic, immune,\ncardiovascular) at different rates; reducing it to a single component oversimplifies complex biological processes and\nignoring the heterogeneous nature of ageing across different organ systems.\nWe agree that work on organ-specific ageing is very exciting, but it asks a fundamentally different research question to\nwhat this paper set out to achieve. There is extensive research that focuses on overarching biological indices of\nphysiological age, just like ours, which we now cite in the paper (e.g., https://pubmed.ncbi.nlm.nih.gov/12634284/,\nhttps://pubmed.ncbi.nlm.nih.gov/19940465/, https://pubmed.ncbi.nlm.nih.gov/19940465/,\nhttps://pubmed.ncbi.nlm.nih.gov/22433233/, https://pubmed.ncbi.nlm.nih.gov/24522464/,\nhttps://pubmed.ncbi.nlm.nih.gov/24659482/ , https://pubmed.ncbi.nlm.nih.gov/3226152/,\nhttps://pubmed.ncbi.nlm.nih.gov/18597867/, https://pubmed.ncbi.nlm.nih.gov/18840798/,\nhttps://pubmed.ncbi.nlm.nih.gov/8803500/, https://doi.org/10.1186/s12889-024-17960-w, ). This work is critical to build\nan understanding about overall associations with physiological ageing before more organ-specific hypotheses could be\ngenerated. So we focused on this within our paper, but we have now made a recommendation that future research\nexplores organ-specific indices within our discussion section.\n• The use of doubly robust estimation with the IPWRA estimator in a cross-sectional study raises some methodological\nconcerns. First, the lack of temporal ordering prevents causal inference, as exposure and outcome are measured\nsimultaneously. Second, unobserved confounders may bias the results. Third, inverse probability weights (IPWs) are more\nappropriate for longitudinal studies, where they adjust for selection bias over time; their use in a cross-sectional setting is\nless effective. Fourth, many covariates, including ses and health behaviors, vary over time, but the analysis relies on a\nsingle time-point measurement. Finally, the cross-sectional design limits the ability to conduct sensitivity analyses to\nassess time-dependent confounding or feedback effects.\nDoubly robust estimations naturally have limitations, but they also have many strengths. They maintain unbiased\nestimates even if one model is miss-specified. They can be more efficient compared to traditional IPW estimation. They\nallow greater flexibility in the choice of models, allowing for better control of confounding. They simultaneously provide\nrelative and absolute effects. There are ample examples of their usage in cross-sectional studies (e.g.\nhttps://doi.org/10.1093/biomet/ass013), with comparative studies suggesting very similar estimates from approaches\nsuch as IPW and adjusted regression models (e.g. 10.1097/EDE.0b013e3181f57571).\nRegarding the cross-sectional issue, we do want to flag that we DID have a longitudinal data component, not only looking\ncross-sectionally but also with data four years later. Naturally, incorporating additional longitudinal data will be an\nimportant part of future analyses to build on the research presented here, but our analyses provide important first data\non this. We have now provided additional discussion of the limitations, discussing the issues raised by the reviewer here,\nand proposed how the work can be taken forwards in the limitations section.\n• The classification of variables as confounders, mediators, or outcomes appears arbitrary and lacks clear conceptual and\nempirical justification. For example, depression could plausibly act as a mediator, confounder, or bidirectional factor in the\nrelationship between social connections and ageing, depending on the theoretical framework. The decision to include\ncertain variables in the exposure vs. outcome confounder models seems inconsistent and requires a stronger theoretical\nrationale.\nWe have now added in substantial additional text and references to justify our theoretical rationale for how we are\ntreating confounders within the methods section.\n• Table 1 lists \"No. of chronic conditions\" as \"Yes/No,\" which seems wrong—a count variable cannot be binary. The authors\nshould clarify whether they measured the number of conditions or the presence of any condition and adjust the\ncategorization accordingly.\nThis was a typo and has now been corrected.\n• The manuscript lacks a full intercorrelation table for all study variables, except for social support. Given the potential for\nmulticollinearity, particularly among ageing-related psychological measures, the authors should provide a full correlation\nmatrix to assess the extent of redundancy and ensure the validity of their models.\nWe have now included an additional correlation matrix for all variables in the study demonstrating no multicollinearity or\nvariable redundancy. This is in Supplementary Figure 4.\n• The pyramid structure used for the social connection model implies an implicit hierarchy or ordering of social relationships\nthat lacks empirical justification. If the model suggests qualitative differences between levels, the authors should provide\nempirical support for this structure. Otherwise, an alternative visualization that does not imply a strict ranking may be more\nappropriate.\nThe pyramid figure is based on existing theory e.g. Holt-lunstad & Steptoe 2022, as structural factors are structural factors are thought to\nunderpin functional and quality factors (see figure below from the paper mentioned for reference). For example, an\nindividual cannot have poor quality social relationships if they are entirely socially isolated. We have now clarified that our\npyramidal structure is based on pre-existing theory in the methods and provided an accompanying reference.\n• Resolution of figures is poor.\nThis has now been updated.\nDISCUSSION:\n• Additionally, the literature review is highly selective, overlooking key studies in ageing, social support, and biological\nageing. The authors should engage more comprehensively with existing research to provide a balanced and accurate\nfoundation for their work.\nWe have now added in substantial amounts of new literature to the paper (over 20 new references).\n• The claim that the study provides \"novel insight into a plausible mechanism\" linking social deficits to ageing is overstated\nand inaccurate. Demonstrating an association between social support and a composite ageing index does not establish a\nmechanism, as mechanisms require causal pathways explaining how social factors biologically influence ageing. The\nauthors should clarify their contribution—if they are identifying correlations, they should not claim mechanistic insight.\nAdditionally, the novelty of this finding is questionable, given prior research on social determinants of(subjective and\nbiological) ageing. Similarly, the claim that these findings \"provide insight into how social deficits influence age-related\npathology\" is overstated. Additionally, the statement that \"objective biological and functional processes may be more\nimportant mechanistically than broader processes related to subjective perceptions\" is unclear and lacks empirical\njustification. The authors should clarify their reasoning, define what is meant by \"broader processes,\" and avoid\nmechanistic claims.\nWe have now removed claims that our study shows new mechanisms as outlined above. We have nuanced statements\nabout the relevance for age-related pathology research too, such as changing the sentence mentioned to “these findings\nmay have a relevance for research focused on social deficits and age-related pathology”, and we have then turned our\nassertions into further hypotheses to be tested in future studies.\nReviewer #2 (Remarks on code availability):\nDid not see the code. The code is all available publicly via OSF. https://github.com/dfancourt/socialdeficits\nReviewer #3 (Remarks to the Author):\nAuthors used ELSA data to evaluate social connections (structural, functional, and quality) on perceived and physiological\naging. Overall, the study was well written and contributes to the literature in a meaningful way. However, I have a few\nrecommendations to strengthen the contribution and justification for the design of the study.\nWe are pleased that the reviewer is positive about the paper.\nIntroduction:\nI would recommend rewording the lines 44-54 as it makes it seem that physiological age is new – even though it precedes\nthe development of epigenetic clocks and metabolic clocks. I would perhaps also focus on the fact these types of clocks\nwere not trained on prediction of outcomes but rather incorporate physiological aging processes themselves (unlike\nepigenetic clocks that were trained on outcomes).\nThank you for this point. This clarification has now been made.\nI am uncertain about the current motivation for perceived age and physiological age. It seems like two different papers to\nme. I would recommend some additional incorporation that may answer is one related to health better than the other?\nHow are they related to each other in other studies since it has low correlation here? Why are both necessary to\nunderstand the mechanistic processes? Giving further motivation here would help to strengthen the contribution of the\npaper and highlight why we need to consider perceived (even if you the authors do not find as robust findings).\nIn line with Reviewer 2’s comments, we have now rewritten the introduction. Our motivation for including both is to\nascertain how much results are due to subjective perceptions vs objective physiological markers of ageing.\nMethods:\nCan the authors motivate the addition of cognitive functioning as a component of physiological age, as cognition is not\npurely a physiological process but a combination of cognitive development and pathology. To justify this use, I might also\nrecommend the authors do a sensitive check without cognitive functioning to keep in line with prior iterations. I do\nrecognize that it is unlikely to change the results as cognitive functioning often runs parallel to the other components, but\nconceptual clarity would be helpful here.\nWe agree that cognitive function is not purely physiological and that including it in a physiological age measure introduces\nconceptual ambiguity. Our rationale for its inclusion was that cognitive decline often parallels physiological deterioration in\nageing and may share underlying biological pathways (e.g., inflammation, vascular health). That said, we recognise the\nimportance of conceptual clarity and have conducted a sensitivity analysis excluding cognitive function from the\nphysiological age composite. The results remain substantively unchanged.\nDiscussion:\nWhat does the role of mortality selection have on these models? Is this a limitation to consider? I appreciate the 4 years\nfollow up but I am still wondering about sample selection and survival and important processes to consider.\nWe agree that the people with the worst social deficits and fastest physiological ageing may be less likely to survive to be\nincluded in the study. For the cross-sectional analysis, this would tend to bias associations toward the null. However, this\nwould mean that if anything, the results underestimate the association between social deficits and physiological ageing.\nFor the longitudinal analysis, survival could technically be considered a collider and potentially therefore induce a spurious\nassociation. Nonetheless, the fact that longitudinal results are consistent with cross-sectional findings suggests this was\nunlikely to be an issue. We now add a new note on this into the limitations section.\nCould the authors also add that people with health conditions may also have more complications with social participation,\nwhich itself may lead to faster aging? I realize that the living alone and living with someone may partly address this\nconcern (especially with severe health conditions) but feel it may be an important note to consider for future work (or\neven a potential sensitivity check).\nWe have now provided an expanded paragraph in the discussion (paragraph 2) where we discuss bidirectionality in much\ngreater detail and explain that while we cannot make causal claims from this data, we do put forward some hypotheses to\nbe tested in future causal studies.\nMinor comments:\nYou write the first aging clocks in the introduction. I would reword to state the first-generation epigenetic aging clocks\nwere trained on chronological age. While not called aging clocks per se, other algorithms like PhenoAge served a similar\npurpose and may confuse readers.\nWe have now revised the introduction in line with comments from other reviewers so this has been removed.\nPlease specify if sample was community dwelling adults. If so, that may also be a limitation to consider. But I am uncertain\nof the commonality of nursing homes in the UK context.\nOur sample focused on community-dwelling adults, so it does not incorporate adults living in care homes. We have now\nlisted this as a limitation, although we have highlighted that only around 2.5% of adults over the age of 65 in the UK live in\ncare homes.\nRESPONSE TO REVIEWERS\nReviewer #2 (Remarks to the Author):\nI appreciate the authors responsiveness. The authors acknowledge the concerns raised. However, the manuscript has\nthe potential for deeper improvement.\nWe are grateful that the reviewer appreciates all of our updates to the paper, and we’re delighted that the other 3\nreviewers are satisfied with the paper now and have approved it for publication. We have made changes for the\nreviewer’s remaining suggestions below.\nPage 2; first paragraph: The revised framing improves clarity but still misrepresents the state of the literature. The claim\nthat physiological aging indices are “providing new avenues” overstates their novelty. These indices have been developed\nand used for over a decade, and many researchers explicitly avoid combining heterogeneous systems (e.g.,\ncardiovascular, inflammatory, functional) into a single composite due to their distinct biological trajectories and limited\nconceptual coherence. Moreover, the field has moved toward more mechanistically grounded approaches such as\nepigenetic clocks, which may reflect upstream aging processes more precisely. The revised paragraph would benefit\nfrom a more balanced acknowledgment that physiological indices are not conceptually new, and their aggregation\nremains contested. This would also temper the overstated novelty in linking them to social variables—something that\nhas been explored, if not always in aggregated form.\nWe have now rephrased this as “alternative avenues”, which illustrates that this is a different type of research from that\ncarried out previously, but removes the potential for overstated novelty. That said, we do want to stress that aggregate\nphysiological indices are novel in relation to social variables and there is increasing acceptance of their importance in\nrelation to complex exposures (e.g. https://www.nature.com/articles/s41591-025-03808-2).\nPage 2; second paragraph: The claim that subjective and physiological age provide “complementary insight into\nmechanistic processes” remains rather speculative without theoretical or empirical justification. A more cautious and\ntheoretically grounded framing is needed to clarify the study’s actual contribution.\nWe have provided substantial additional theoretical framing in our introduction for why subjective and physiological age\nprovide potential mechanistic insight. In response to previous comments, we clarified in this paragraph that we are not\ntesting the mechanism specifically, but taking a first step towards such a test by looking at the association between\nsubjective and physiological age and weak social connections. We have been cautious in providing this explanation and\nin our discussion in interpreting our findings. But we do believe it is important to keep this sentence in the introduction\n– it explains clearly and concisely why this work is necessary and where it can lead, which is important for readers to\nunderstand the trajectory of this line of research.\nThe authors acknowledge concerns about heterogeneous aging dynamics but largely dismiss them by relying on visual\ninspection using local polynomial regression. While the added plots in the Supplementary Materials are appreciated,\nthe justification remains insufficient. Local polynomial smoothing is, as the authors note, prone to overfitting and does\nnot provide formal tests of nonlinearity. More importantly, visual linearity within a constrained age range does not\nnegate the conceptual issue: the biomarkers reflect distinct physiological systems with inherently different temporal\ntrajectories and exposures. Simply showing approximate linearity does not resolve the concern about combining them\ninto a single metric.\nThe reviewer makes two points here, relating to whether it is statistically appropriate and conceptually appropriate to\ninclude different physiological systems within a single ageing metric. Statistically, we have now undertaken even more\nchecks on this issue. We have formally assessed whether age-biomarker associations were linear by fitting restricted\ncubic splines (knots at the 5th, 35th, 65th, and 95th percentiles of age in the analytic sample). Likelihood ratio tests of\nnonlinearity were statistically significant for three of eight biomarkers, reflecting departures from a strictly linear\nrelationship with age. However, consistent with the local polynomial plots, spline-based plots showed that in the analytic\nage range all biomarkers increased monotonically with age. These findings indicate that while the large sample size\nmakes even small deviations from linearity detectable, the functional forms are close to linear and monotonic. This is\nimportant because the PCA-based physiological age index does not require perfectly linear relationships, only that\nbiomarkers show consistent age-related change. Monotonicity ensures that individual ordering is preserved, so the PCA\nis robust to these minor nonlinearities. We have now replaced local polynomial plots with spline-based plots in the\nsupplementary materials, and added a supplementary table with p-values for formal tests of linearity. Conceptually, there\nis substantial precedent for indices such as the one presented here, which we already cite as support for the\napproach presented here. We have provided an extended list here, including a recent paper from Nature Medicine, to\nshow that such indices are being widely used:\nhttps://pubmed.ncbi.nlm.nih.gov/12634284/, https://pubmed.ncbi.nlm.nih.gov/19940465/,\nhttps://pubmed.ncbi.nlm.nih.gov/19940465/, https://pubmed.ncbi.nlm.nih.gov/22433233/,\nhttps://pubmed.ncbi.nlm.nih.gov/24522464/, https://pubmed.ncbi.nlm.nih.gov/24659482/ ,\nhttps://pubmed.ncbi.nlm.nih.gov/3226152/, https://pubmed.ncbi.nlm.nih.gov/18597867/,\nhttps://pubmed.ncbi.nlm.nih.gov/18840798/, https://pubmed.ncbi.nlm.nih.gov/8803500/,\nhttps://doi.org/10.1186/s12889-024-17960-w, https://www.nature.com/articles/s41591-025-03808-2\nThe inclusion of memory as part of the physiological aging index remains conceptually problematic. While cognitive\ndecline is age-related, it is not equivalent to brain age, nor is it typically considered a direct marker of physiological aging.\nBrain age clocks rely on structural and functional neuroimaging or molecular data—distinct from behavioral measures\nof memory performance. Equating memory scores with brain age in their line of reasoning conflates separate constructs.\nAlthough the authors conducted sensitivity analyses excluding cognition, the rationale for its inclusion still lacks\ntheoretical clarity. The manuscript should more explicitly acknowledge these conceptual distinctions and avoid\noverstating the relevance of memory as a physiological indicator. Relatedly, the term “memory” is too general to\naccurately describe the cognitive measures used in the index. Tasks assess specific aspects of episodic memory. Given\nthe differences between memory domains—and their distinct aging trajectories—the manuscript should specify that\nthe measure reflects episodic memory performance.\nWe appreciate that the reviewer is still concerned about the inclusion of memory in the index, and understand the\nrationale given for its exclusion. In response to this comment, we have now taken memory out of the index. The tables\nand figures are now updated to reflect this. The new results are materially very similar to those before, so our conclusions\nare not affected.\nFigures 3 & 4: The x-axis label needs to be updated to the new terminology - “differences in perceived age (years)”\nThis has now been done.\nPage 12, 2nd paragraph: The revised section introduces useful nuance by acknowledging bidirectionality and the limits\nof causal inference, but it still leans too heavily on speculative interpretation. Moreover, positing this as a “potential\ncausal mechanism” risks overstating the implications of what remains correlational evidence—particularly given the\ncomposite nature of the physiological index and the lack of temporal resolution. I recommend more cautious wording\nthat emphasizes hypothesis generation rather than mechanistic interpretation.\nWe respectfully note that this section is not presented as an interpretation of our findings, but rather as a hypothesis\nfor future research. We have already carefully revised the text to state explicitly that this is hypothesis generation and\nnot an interpretation of our results, for example: “While this study (which focused only on cross-sectional and some\ninitial longitudinal data) cannot make causal claims nor confirm such a mechanism, we put forward the hypothesis here\nas the basis for future research.” We also clarify that this is hypothesis generation rather than mechanistic interpretation\nin our discussion section. We hope that this cautious wording is now clear to the reader."}]

Decision letter